One of the first things to come to mind with the word “thunderstorm” is lightning. As is the case with most thunderstorms, the presence of lightning is dictated by the level of instability in the atmosphere and, more intensively, the intracloud interactions of graupel, or supercooled water droplets that rime together but are not quite as solid as hail stones. Keep in mind that these processes occur well above the surface of the earth (approximately anywhere from 35,000 to as high as 70,000 feet above the surface) and are captured by the Geostationary Lightning Mapper (GLM) onboard the Geostationary Operational Environmental Satellite (GOES). These lightning observations can provide crucial evidence into the development/maturity of a thunderstorm over the course of its lifetime. But this information is most important for the forecasters who ultimately digest the information outputted by the models to come up with as highly accurate a public forecast as possible.
To understand the importance of how assimilating lightning data models can potentially enhance a forecast, we must first understand how models ingest this lightning data. Now, data assimilation is a complex topic that, for brevity, I will summarize in just a few sentences. The general idea of assimilating data into a model is to be able to provide extra information otherwise unknown to the model so that it can compute a forecast (known specifically as a posterior). A weather model is only as good as the assumptions it has at the “analysis” time, or start time, so more data could give the model more to consider as it integrates through time. The data that goes into these models must be quality controlled as the amount of noise that the raw data can contain can alter the model’s perception of the state of the atmosphere. In other words, if a model feeds on bad data, it will produce a bad forecast, and this occurs in a compounding loop until the quality of the data is improved on. Regarding assimilating lightning data, the model can use the available data to help it evolve thunderstorms in the short-term. Whether they could intensify or weaken could be highlighted in clues within the storm’s total lightning count (more precisely, the flash extent density or FED). The FED is the most common GLM-derived product to be assimilated as it serves to show convective intensity and is often correlated against graupel interactions within the cloud, so it serves as a good example of an intensifying or weakening storm. Studies completed over the last few years have shown an appreciable gain of ingesting quality-controlled lightning observations from the GLM. For instance, a study by Fierro et al. (2019) examined the benefits of assimilating GLM lightning-derived water vapor data and radar data during short-term forecasts (usually no further than 6 hours in time). They found modest improvements in the accuracy of the forecasts. Similar studies of organized supercell cases (e.g., Fierro et al. 2016; Mansell 2014) have also demonstrated improvements of forecast accuracy and statistical gains for success scores. In short, there is much to be learned (and possibly gained) from assimilating GLM lightning observations! A great article to understand more about the lightning process is given here by the folks at the National Weather Service: https://www.weather.gov/jetstream/lightning To learn more about other weather research topics, be sure to click here! Image credit above: NWS Jetstream © 2020 Meteorologist Brian Matilla
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 Countless areas across the country are exposed to the lack of instrumentation data and lack of radar coverage. They are usually less populated areas, but they are also vital for forecasting possible severe weather events and can help gather data for weather research. This is where a rather interesting weather instrument comes into play: the mobile mesonet. A mobile mesonet is a mobile HUB for weather data, fixed with numerous weather instruments such as an anemometer, wind vane, GPS, temperature sensor, humidity sensor and pressure sensor. As pictured above, all of these instruments are attached to an SUV. Within the SUV, there are three computer monitors each connected to WIFI within the vehicle and directly connected to the data that is being collected with the use of the instruments. Also, HAM radios are installed for cross-communication with other people in the field. Each seat, besides the driver, is equipped with said computer monitors. With each seat, comes a different role. The passenger seat is the navigator and radar observer. This person’s job is to navigate the driver while also keeping an eye on the radar for potential weather that can be relevant to the mission. Rear driver-side is the data collector. This person keeps an eye on the data and makes sure it is being collected properly and stored in the right manner. Rear passenger is the field note keeper. This person keeps a running log of any field notes while on a deployment. Also, this person can aide the passenger-seat while observing the radar. Central Michigan University received one of these mesonets in June 2019 for research in boundary layer meteorology, more specifically in lake breeze fronts. Conducted by Dr. Jason Keeler of Central Michigan University, the goal of CMU-STORM was to intercept lake breeze fronts and observe how they interact with localized weather. Also, to teach students on how to utilize instruments for meteorology research. Hence the name STORM, which actually stands for Student Training for Observational Research in Meteorology. With Lake Michigan to the west of Central Michigan’s campus, this was an ideal area for this research.
CMU-STORM deployed over a two-week period in July. From Traverse City, MI to Three Oaks, MI, the STORM team traversed up and down the Lake Michigan coastline gathering data on boundary layer meteorology. Numerous lake breeze fronts were observed and logged using the data from the instruments. Interactions between these lake breeze fronts and local weather were observed and logged using the weather instruments. Being able to exist in the medium that was being studied brought the team closer to what they wanted to accomplish, all while gathering rare meteorological phenomena. During one of the deployments, a storm system made its way through central Michigan, bringing heavy rain, frequent cloud-to-ground lighting and strong winds. This storm system was tapping into the warm and moist air in the region. Theta-E, which is a measurement of the stability of the air, is directly related to temperature and dew point. As temperature and dew point goes up, so does Theta-E. A little known weather phenomena called Mesoscale Air mass with High Theta-E (MAHTE) was observed during this deployment. This is an area of meteorology which needs more research to accurately understand it. Essentially, a MAHTE occurs when the cool side of an air mass boundary has more instability than the warm side of the air mass. Typically, the warm side of the air mass has more instability than the cool side. This is because warm and moist air is typically more unstable than cooler air. This rare occurrence can result in storms being produced in areas where storms wouldn’t typically grow. Like said above, MAHTEs need more research and data for they are still a relatively unknown meteorological feature. CMU-STORM presented data that couldn’t be found anywhere else or with any other instrument. Essentially, the mesonet was the pencil connecting the dots between weather stations filling in gray areas. The mesonet is a vital tool for forecasting and being able to see areas that are not represented with a weather station. The data collected from the deployment is still being observed and utilized for a better knowledge in boundary layer meteorology, especially research concerning MAHTE. For updates and more information on CMU-STORM, stayed tuned because CMU-STORM 2 launches this summer 2020 in June and July. For more weather research topics click here. ©2020 Weather Forecaster Alec Kownacki DISCUSSION: According to NASA and NOAA the year 2019 was the second warmest on record, coming in at a little more than 0.6o C above the 1981 - 2010 average. This year also began with El Niño conditions in place over the eastern Tropical Pacific Ocean. These conditions held well into the spring and early summer, and even into fall as the tropical Pacific was classified as warm neutral by the end of the year. In spite of the warm year globally, much of the central USA experienced cool conditions during February and March 2019. Also, a strong cold spell dominated much of the central USA in late October and early November. The result was an unusual Halloween snowfall over Missouri. This was associated with a moderate east Pacific blocking event. For the fourth consecutive year, we perform an overview the blocking occurrences in 2019 using the University of Missouri blocking event archive (http://weather.missouri.edu/gcc). We will examine the blocking occurrences for each region of the Northern Hemisphere (NH) and Southern Hemisphere (SH) separately, and discuss a few recent trends in blocking activity. While the NH was a bit active, the SH was very active continuing a trend toward more active blocking years globally (e.g., Kononova and Lupo 2020). a.The Northern Hemisphere During 2019, 45 blocking events occurred over the entire NH, which is lower than last year’s total (50) and within the typical range of the mean for the early 21st century occurrences (38). Since we typically expect +/- 8.5 events, 2019 was not unusual. The persistence of 2018 blocking events was similar to their climatological mean for early 21st century blocks (about 9 days), but the intensity was greater than the climatological mean strength. The year 2019 was dominated by a negative Arctic Oscillation Index (AO) (https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/month_ao_index.shtml). A negative AO indicates Northern Hemisphere flow that is strongly meridional or characterized by high amplitude waves in the jet stream. This may account for some very strong blocking events in 2019. As a consequence, we noted some unusually warm and cold periods regionally. However, what was noteworthy about the year was that from the last week of November 2019 into January 2020 featured only a few weak, short lived events. Over the Atlantic Region (80o W – 40o E longitude) in 2019, there were 19 blocking events that occurred which is slightly more than the regional mean. The Atlantic accounted for more than 40% of the NH total activity this year. During 2018, 13 of 22 Atlantic Region blocking events occurred over Eastern Europe and Western Russia. This year, the pattern reversed as only 6 of the 19 blocks occurred over the eastern part of the region. The Atlantic Region blocks persisted for about 9 days and these events were as strong on average as the typical regional event. However, in this region, there were very strong long-lived blocks early in the year, and weak shorter-lived ones later. Two noteworthy events occurred over the United Kingdom in mid-to-late February. The second of the two was a very strong blocking event and associated with the warmest February day in UK history. Fig. 1a shows that most of Europe featured much above normal (+6o C) during this month. The Pacific Region (140o E- 100o W) was quite active (17 blocking events) was compared to the climatological normal (12) in number. The duration was a little less than typical (8-9 days versus about one more than this climatologically). These events overall were stronger than typical, but like the Atlantic Region featured strong events early in the year, and weaker events later. For the third consecutive year, most of these blocking events (14) occurred over the Northeast Pacific and distributed throughout the year. This resulted in very warm conditions as exemplified in Fig. 1a that saw +10 oC temperature anomalies over Northern Alaska. During February, two noteworthy blocks occurred over the Northeast Pacific. These also forced the very cold temperature anomalies over Canada and the Northern USA. The second of these events was a 15-day blocking event that lasted into March and was extremely strong. The same temperature anomalies persisted for March, but were not as strong. Nunes et al. (2017) and references therein show extreme cold over North America is typically associated with blocking in the eastern Pacific Region. Additionally, during late February 2019, North America was sandwiched in the middle of a very powerful NH simultaneous blocking episode. Another big story in the NH during this year was the very hot temperatures that led to “Baked Alaska” during late June and into July (Fig. 1b). Note that the temperature was 4-5 oC higher than normal over Southern Alaska resulting in a record high of 32 oC in early July. This was associated with a “heat dome” over Alaska. A blocking event from 27 June – 11 July persisted over the region. This is long-lived and the event was stronger than the typical summer season event. The conditions were similar to those of August 2004 that resulted in hot dry conditions and record fires (Hussain and Lupo, 2010).
Figure 1. The Northern Hemisphere surface temperature anomaly (oC) for a) February 2019 (left), and b) July 2019 (right). In 2019, the Continental Region (40o – 140o E) experienced nine blocking events which close to the mean of eight events. These events all occurred from May – November over various parts of Eurasia. Also, this means the Continental Region was less active than the previous two years. These events were typical in duration and strength. The warm conditions over Southern Russia and Afghanistan during July (see Fig. 1b) may have resulted from a strong long-lived summer event in this region (12-24 July). For the third consecutive year, no blocking occurred over North America in 2019. As shown in many studies, the occurrence of blocking over North America is comparatively rare. a.The Southern Hemisphere In 2019, the SH was nearly as active (26 blocking events) as 2018 (27 events), the second most since 1970. Last year, blocking was quite active in the Indian Ocean Region (30o E – 130o E). This year, there were only two events in both the Atlantic and Indian Ocean regions. This is more typical of SH blocking. Most of the blocking (22 events) occurred over the Pacific Region, which is the highest in the 50-year period (1970 – present). Wiedenmann et al. (2002) demonstrated that most blocking events occur in the South Pacific and during the months of May and June. In the SH overall, the blocking events were close to climatological mean strength (2.94 vs. 2.85), but about one day shorter than the mean duration (under 7 days vs under 8 days). The record setting year in the South Pacific was paced by the occurrence of 22 events over the South Pacific, and their mean duration and strength was similar to the hemispheric means. The long-term anomalies in duration and strength were identical to the SH overall, which is not surprising since the Pacific region was so dominant. This year, the peak occurrence of the blocking events (10) was April to July, which is normal. For the third consecutive year, spring was fairly active (Oct- Dec – seven events) which is unusual. Last year most Pacific Region events occurred over the southwest Pacific, but this year more blocking events (14 of 22 Pacific Region events) occurred over the southeastern Pacific. These events were typical in duration, but weaker than normal. Blocking in the southeast Pacific will lead to colder conditions over southern South America. Fig. 2a shows the impact of four late winter events that occurred over a 37-day period from 15 August – 21 September 2019. Two of these events were quite strong for the SH leading to the cool September over South America. The early September event in the southeast Pacific was the 9th strongest in the 1970-2019 period.
Figure 2: The Southern Hemisphere surface temperature anomaly (oC) for a) September 2019 (left), and b) surface soil moisture anomaly for December 2019 (right). Australian heat was once again in the news from late 2019 into early 2020. Fig. 2b shows the composite soil moisture for the SH, and it is noteworthy that the entire Australian continent was dry. The months leading up to December were not unusually warm, but these months were also dry. The result was catastrophic fires continent-wide which devastated forests, degraded human health over large areas, and caused the deaths of people and scores of wildlife. There did not seem to be a direct link to the occurrence of blocking, but this weather was noteworthy nonetheless.
In summary, the number of blocking events globally was 71 events compared to the global normal of 55 events. The active year was driven mainly by the occurrences of blocking in the SH. However, the year finished relatively quietly in December. In both the NH and SH, the Pacific Regions were more active than normal with record activity in the SH Pacific. The other regions in each hemisphere were closer to their climatological mean values. Only one event, a southeast Pacific event in September cracked the top 10 strongest events in the climatological data. This event came in at number 9 (BI=4.75) all-time. For the most part, the duration and intensity of blocking in both hemispheres were very consistent with those which have occurred since 2000. Finally, blocking episodes were at least partly responsible for anomalous warm temperature conditions over Europe during February and March. The central part of North America was quite cold over this same time period due to blocking in the Pacific and Atlantic region. An active northeast Pacific kept Alaska and far east Russia warm for most of the year. The active southeast Pacific resulted in a cold late winter over South America. References: Hussain, A., and A.R. Lupo, 2010: Scale and stability analysis of blocking events from 2002-2004: A case study of an unusually persistent blocking event leading to a heat wave in the Gulf of Alaska during August 2004. Advances in Meteorology, 2010, Article ID 610263, 15 pages doi:10.1155/2010/610263. Kononova, N.K., and A.R. Lupo, 2020: Dynamics of the global atmospheric circulation and climate change. Atmosphere, 11(2), under review. Nunes, M.J., A.R. Lupo, M.G. Lebedeva, Y.G. Chendev, and A.B. Solovyov, 2017: The occurrence of extreme monthly temperatures and precipitation in two global regions. Papers in Applied Geography, DOI: 10.1080/23754931.2017.1286253. Wiedenmann, J.M., A.R. Lupo, I.I. Mokhov, and E. Tikhonova, 2002: The Climatology of Blocking Anticyclones for the Northern and Southern Hemisphere: Block Intensity as a Diagnostic. Journal of Climate, 15, 3459-3473. Anthony R Lupo is a professor of Atmospheric Science specializing in the study of blocking anticyclone and jet stream dynamics at the University of Missouri and contributor to The Global Climate and Weather Center.  Modeling is crucial to meteorology. We all rely on the output of the various computer models and their unique configurations, and subsequently the human forecaster skill and intellect, to provide the most accurate and reliable short and long-term forecasts. When it comes to the long-term models, the global forecast system (GFS) and European Center for Medium-range Weather Forecasting (ECMWF) systems are the two predominant long-term, large-domain systems in operations at the moment. The High Resolution Rapid Refresh model (hereafter HRRR), a model aimed at predicting weather at a shorter spatiotemporal scale, has undergone significant changes since its initial implementation into the operational meteorology world in 2012. The HRRR The newest version of HRRR, version 4, is expected to launch into operations beginning in spring of 2020.
So what is new with this upcoming version? For brevity, One of the more significant changes to the model lies in the configuration. The HRRRv4 is expected to run on the Advanced Weather Research and Forecasting dynamical core (WRF-ARW) version 3.9. Several physics changes are expected to be introduced, including enhancements to the Mellor-Yamada-Nakanishi-Niino planetary boundary layer scheme for improved resolution of sub-grid-scale clouds and cloud condensation and ice parameters. Aerosol source-and-sink terms for fire, dust, and smoke particulates. The land-surface/snow model and gravity-wave drag terms are also enhanced. The HRRR from the start utilized the Gridpoint Statistical Interpolator (GSI) data assimilation technique, and HRRRv4 enables the use of GOES-16 sky radiances. This addition is important because satellite-based radiance observations are critical for determining cloud-top temperatures which can potentially give clues to cloud top heights and thunderstorm intensities. HRRRv4 retains the 3-km grid spacing and 36-member configuration to generate a 1-hour pre-forecast with all available data assimilated but now will include the use of METAR cloud observations. Lastly, the MODIS albedo scheme will be replacing the 1-degree albedo. HRRRv4 was tested this past spring at NCEP during the 2019 Spring Forecasting Experiment. Going forward into 2020 and beyond, another set of sweeping changes is expected to be rolled out for the HRRR. A fundamental change forthcoming will be the transition away from the WRF-ARW dynamical core to a unified modeling dynamical core known as the finite-volume cubed-sphere (FV3) worked on by the Geophysical Fluid Dynamics Laboratory. Intended to increase computational efficiency, it is already in full use within the global forecast system (GFS) since mid-summer 2019. It is expected that the HRRRv4 will be the last to use WRF-ARW in favor of FV3. Additionally, the phase-out of the GSI data assimilation scheme will occur in favor of a new unified DA framework known as the Joint Effort of Data Assimilation Integration (JEDI), with one key advancement being the integration of modernized programming techniques while retaining the speed and reliability of traditional lower-level programming code even with finer-resolution data which can take up far more space. Early tests with the JEDI framework show enhancements to several observation components with reduced observation-to-model errors. Also, the push towards finer resolution is allowing for some other model suites to take form, such as the National Severe Storms Laboratory Warn-on-Forecast system. Sub-hourly data assimilation and short-term forecasts to 6-hours will help to provide forecasters and researchers an enhanced insight into potentially high-impact, hazardous weather. So as one can see, there is much to look forward to in the world of meterorological modeling that may enhance forecast accuracy and reliability. Within the next 3-5 years, it will be interesting to see what unfolds and how the modeling world will continue to grow and expand for the betterment of research, forecasting, and the general public. Happy New Year’s Eve to all! To learn more about weather research and other interesting topics, be sure to click here! © 2019 Meteorologist Brian Matilla 
Image: UT Dallas
DISCUSSION: The Boundary Layer and Subsonic Tunnel (BLAST) is a revolutionary
wind tunnel that was unveiled in 2018 at the University of Texas at Dallas. It is expected that this wind tunnel will provide a host of opportunities for students, in various programs. According to Dr. Mario A. Rotea, head of the Department of Mechanical Engineering, “BLAST will be integrated into our curriculum. It will be a key asset for senior design projects that require testing the forces that the wind exerts on objects.” The University of Texas at Dallas indicates BLAST offers a 26,000-pound fan which provide simulations of airflow around various objects and of similar atmospheric conditions, in addition, two test sections in which speeds can reach an estimated 115 mph, that similar to a Category 3 hurricane on the Saffir Simpson scale. Applications of the BLAST wind tunnel include research regarding drag reduction on vehicles, lift production in airfoils, effects of wind on construction materials, and even used for turbine production. Wind tunnels provide a range of capabilities, making it an ideal experimental piece for industries ranging from aviation, atmospheric science, agriculture, automotive and even the Olympics. These industries could ultimately benefit from such research by continuing to investigate the wind dynamics in various instances. For other articles on research or meteorological instrumentation visit the Global Weather Climate Center! © Meteorologist Jessica Olsen  DISCUSSION: When it comes to studying hurricanes, there are several methods by which atmospheric scientists and researchers evaluate and determine the evolving intensity of hurricanes. In going all the way from geostationary orbiting satellite platform observations to observing stations located both on land and across parts of the ocean, there sometimes is a need for an even more in-depth understanding of what is happening within given storms.
One of the more precise ways in which we determine the intensity of tropical cyclones is by launching instruments known as dropsondes into various parts of given tropical cyclones. Dropsondes are devices which are strategically launched from reconnaissance aircraft into various parts of tropical cyclones to study things such as moisture profiles, wind profiles and pressure fields within said storms. Dropsondes are so important since dropsondes help scientists and forecasters to verify what they may or may not think they are seeing and interpreting via various satellite imaging platforms. Moreover, dropsondes allow scientists to get a more exact understanding of how strong a tropical cyclone is at different points in the storm. This can be and often is accomplished by launching upwards of 10 to 20 dropsondes within a given mission in order to gain a more complete understanding of what is physically happening in different parts and at different heights in a storm at a given point. So, even after the all of the debate as to what is exactly the best possible method by which atmospheric scientists can and should attempt to study tropical cyclones in the future, it is hard to debate whether there is a better option than using dropsondes. However, the one legitimate argument which can be made for the further advancement of science is to integrate a newer technology such that there would be a bit less in the way of additional littering to the global ocean systems (even as minimal as it is from these dropsondes). Despite this issue, they still play an incredibly important role in the global atmospheric science research community in being able to learn an increasingly greater amount about how these storms operate from a dynamical standpoint. To learn more about other weather research topics from around the world, be sure to click here! ©2019 Meteorologist Jordan Rabinowitz 
DISCUSSION: As we head further into the 21st century, there is no question that day-to-day operational forecasting has underwent tremendous changes to improve daily weather forecasts. However, what many people do not always realize is the reason for why atmospheric and computer science research scientists constantly strive to continually implement stronger and stronger improvements to numerical model forecast techniques and approaches thereof. It is therefore that much more important to understand this issue to have a renewed sense of appreciation for why numerical model improvements are so highly valued by people throughout the global geoscience community.
First off, improvements to numerical models allow atmospheric scientists to conduct increasingly more realistic simulations of the “true” atmosphere as it exists on planet Earth. Having said that, it is CRITICAL to recognize that no single numerical model forecast (e.g., the Global Forecast System (GFS), the North American Model (NAM), the European Model (ECMWF), or the Weather Research and Forecasting Model (WRF)) will NEVER produce a truly perfect simulation of any weather event (past, present, or future). However, such numerical models can lend us insights into understanding how different types of model set-ups and/or formats can give us a better or worse understanding for how and why given weather events may unfold or may have unfolded in the way that they did. Thus, the more and more the global geoscience community continues to work to further improve numerical model accuracy, the farther that current and future research may be able to go (i.e., regarding the development of an even more in-depth understanding of how different aspects of Earth’s atmosphere physically work). Another major factor which comes along with improving specific components of numerical models is the fact that you can often include more detailed representations of different processes which in many years were very hard to represent in any capacity. Thus, the more and more details of the atmosphere which can be included into a given numerical model’s formatting, the more reliable that a given numerical forecast model can be for forecasters in an operational setting. Hence, the next time you have a random conversation about weather and/or weather forecasting with friends, family, or anyone, the better appreciation you will now have about why improvements to numerical models are so highly valued by the global geoscience community. Attached above is a link to some of the earlier details released regarding the recent GFS model upgrade which should certainly help to further improve various aspects of global weather forecasting. To learn more about other weather research topics from around the world, be sure to click here! © 2019 Meteorologist Jordan Rabinowitz  Time and time again, the world of meteorology continues to find new avenues to explore in terms of representing the state of the Earth as we know it. The advent of more powerful supercomputers and more advanced algorithms give scientists more information and finer precision on details such as surface temperature, wind speed at various altitudes, and rainfall accumulations among others. It is also the plethora of available data that makes it increasingly challenging for forecasters to monitor all available data all of the time. So what if algorithms could be created such that they can attempt to predict future states of the atmosphere based on already recognized patterns of cloud types or storm shapes? Machine learning can help to accomplish this task. Machine learning is a technique in which the model can effectively attempt to guess future outcomes based on spatial pattern recognition from previous data (analogs) and then “learn” and adapt the forecasts with the purpose of enhancing forecast accuracy and increased likelihood of forecast verification.
A very recent article accepted for publication by Weyn et al. (2019) in the American Geophysical Union’s Journal of Advances of Modeling Earth Systems broaches the subject of predictability by virtue of a concept known as a convolutional neural network (CNN). Here, the authors construct a model in which past meteorological data is used to “train” the model to then predict mid-level atmospheric heights with no knowledge of ongoing physical processes. The experiment consisted of several schemes The results are optimistic in that the CNN is able to outperform basic climatology and persistence forecasts with a lower root-mean-squared error for lead times between 3 and up to 14 days from forecast initialization. Times beyond the short-term do introduce sensitivities into the model that may lead to degraded physical representations of the atmosphere as the authors note, but present-day forecasting techniques are also subject to possibly larger errors with greater lead time. Now, most of the machine learning/artificial intelligence practices are still in the experimental phase as there is still much to learn about how to apply the techniques to real-time forecasts. As there are multiple components that feed into a forecast such as data assimilation, model physics, etc., present-day forecasting still has quite the ways to go before becoming a feasible option for daily use in the field. In addition, machine learning can be a cost and computer resource-intensive approach that may not be the most ideal approach given current resources. However, machine learning techniques are already making inroads in the retrospective analysis realm (reprocessing previous events and their forecasts). These retrospective analyses could become the building blocks for where to go in the near future in advancing machine learning approaches for weather forecasting. Applications of retrospective forecasting of small-scale events is discussed in a relatively recent paper by Gagne et al. (2014) for which they applied machine learning techniques to increase reliability of precipitation forecasts. Even so, machine learning also has potential applications to climate forecasting as the variability scale for climate versus weather is much smaller in time. Nevertheless, artificial intelligence via machine learning is an increasingly considered tool in weather and climate forecasting and while there are still several factors that are limiting a full-scale rollout of these techniques into operations, the benefits of recent research and promising results means that machine learning could ultimately become a useful tool in the arsenal for forecasters and researchers alike. Here are the appropriate citations for each article above: Gagne, D. J., A. McGovern, and M. Xue, 2014: Machine Learning Enhancement of Storm-Scale Ensemble Probabilistic Quantitative Precipitation Forecasts. Wea. Forecasting, 29, 1024-1043, https://doi.org/10.1175/WAF-D-13-00108.1 Weyn, J. A., D. R. Durran, and R. Caruana, 2019: Can machines learn to predict weather? Using deep learning to predict gridded 500-hPa geopotential height from historical weather data. J. Adv. Model. Earth Sy,. Accepted., https://doi.org/10.1029/2019MS001705 Image credit above: NCAR Research and Applications Lab To learn more about other weather research topics and issues, be sure to click here! © 2019 Meteorologist Brian Matilla 
DISCUSSION: Field campaigns in the atmospheric sciences play a vital role in allowing forecasters and researchers to obtain further knowledge on the evolution of the state of the atmosphere. This is especially true in situations where high impact weather is expected to occur over densely populated areas. This year, The National Severe Storms Laboratory launched the Targeted Observations by Radars and Unmanned aerial surveillance of Supercells (TORUS) experiment designed to investigate the dynamics of ongoing severe thunderstorms and how their evolution may lead to the potential formation of tornadoes. The focus of the TORUS experiment is within the planetary boundary layer – the lowest layer in the atmosphere influenced by the frictional force of the wind, and how its constant changes could impact nowcasting and forecasting of ongoing severe weather threats. Much like the Mesoscale Convective Experiment (2013) and Plains Elevated Convection At Night (2015) experiment, an overarching goal of TORUS was to advance the current knowledgebase of supercells and how changes in their intensity as a function of the surrounding environment can help enhance or degrade a public forecast while also serving as extra information to ingest into weather models for enhanced predictability at relatively short time scales. The combination of ground-based equipment and techniques along with the use of NOAA’s Lockheed P-3 Orion “Hurricane Hunter” should return plenty of information.
Normally, the National Weather Service Weather Forecast Offices (NWS WFOs) launch radiosondes at 1200 and 0000 UTC (e.g., 8 AM/8 PM EDT) to sample the overall atmospheric profile and obtain valuable information for the prediction of severe storms. One particular case in where forecasters benefitted from the additional data supplied by TORUS was with the most recent high risk severe weather day on 20 May. The mobile soundings provide extra atmospheric data at unconventional times in the day that are the stepping stone to potential modifications for a forecast. For instance, a TORUS sounding taken near Vinson, OK at around 1930 UTC (2:30 PM CDT) showed a highly unstable environment and highly favorable for the development of significant supercells that could have led to long-track, violent tornadoes. Compared to a more traditional 1200 UTC sounding, that meant that over seven hours had elapsed which is ample timing for the environment to change considerably. Ultimately, this was not the case across much of central and southern Oklahoma on 20 May for which the reasoning is still up for much debate to this date, but it is information like these soundings that give forecasters a leg up on making the necessary quick decisions with a rapidly changing environment. Even with the ability to interrogate the atmosphere with mobile soundings, it also elucidates more questions and unknowns for researchers to grasp onto moving forward. For example, why did the 20 May severe weather outbreak not materialize as expected despite environmental parameters suggesting an outbreak akin to the 27 April 2011 tornado outbreak over the Deep South states of Mississippi and Alabama? Or, how is it that “lower” severe weather risks issued by the Storm Prediction Center lead to more active days? These are good questions to ponder about and while mobile soundings may not provide the entire story to the eventual growth and while the world of research to operations (commonly known as R2O) has much to learn about sub-daily (and even sub-hourly) forecasts, field campaigns like TORUS provide the necessary benefits for forecasters and researchers alike to gain a richer understanding of quickly evolving atmospheric conditions. More about the TORUS experiment can be found here. To learn more about other weather research topics and issues, be sure to click here! Sounding credit: Manda Chasteen © 2019 Meteorologist Brian Matilla  DISCUSSION: In looking back to August 2017 and the Atlantic hurricane season, there is no debate that one of the events which stood out among the rest quite a bit was Hurricane Harvey. Hurricane Harvey is arguably most remembered for its impacts with respect to the major flooding the storm delivered in and around the city of Houston, Texas. However, the other major aspect of Hurricane Harvey which made headlines for quite a while was how the storm rapidly intensified from a Category 3 to a Category 4 just before making landfall in southeast Texas. There continued to be substantial interest in this topic which inspired and ultimately led to more comprehensive research on the issue from both academic institutions as well as various weather and climate research agencies. The main conundrum is the fact that as hurricanes approach shallower coastal waters and continue to induce substantial oceanic mixing, this brings up much cooler water which often helps to weaken the storm or stabilize any further intensification. However, this is not what transpired with Hurricane Harvey and hence, the reason for why much further research was needed. One such example of this research came from a research group over at Texas A&M University.
In this research, “They found the Bight was warm all the way to the seabed before Harvey arrived. Strong hurricane winds mix the ocean waters below the storm, so if there is any cold water below the warm water at the surface, the storm's growth will slow. But there wasn't any cold water for Harvey to churn up as it neared the coast, so the storm continued to strengthen right before it made landfall, according to the study's authors. "When you have hurricanes that come ashore at the right time of year, when the temperature is particularly warm and the ocean is particularly well-mixed, they can absolutely continue to intensify over the shallow water," said Henry Potter, an oceanographer at Texas A&M and lead author of the new study in the American Geophysical Union's Journal of Geophysical Research: Oceans. The researchers don't yet have enough temperature data to say if the Texas Bight was unusually warm in 2017. But the findings suggest hurricane forecasters may need to adjust the criteria they use to predict storm intensity, according to Potter. Forecasters typically use satellite measurements and historical data to make intensity predictions, but Harvey's case shows they need data collected from the ocean itself to know exactly how much heat is there, where that heat is located in the water column and if it's easily accessible to the storm, Potter said.” To read the full story, feel free to click here! To learn more about other weather research topics/issues, be sure to click here! © 2019 Meteorologist Jordan Rabinowitz 
DISCUSSION: For many years, weather forecasting has proven challenging for forecasters and researchers. Marked by consistent improvement yet continual obstacles, the nature of forecasting any type of weather event from benign showers to a full-scale severe weather outbreak is loaded with stochasticity. But at the core of weather forecasting, two schools of thought dominate the practice: deterministic and probabilistic forecasting. Each one of these is subtly different at the surface, but fundamentally they have their characteristic differences.
Deterministic forecasts are based specifically on a given value or range for an area at a given time (e.g., temperature at morning rush hour or evening commute). This is the kind of product we are used to seeing on forecast bulletins and on local news media. Examples of a deterministic forecast include the first tweet above with a range of values for potential ice accumulation over central Oklahoma. A precise value or time is important for the general public as it gives people a frame of reference for what to expect. Probabilistic forecasts take on a different approach and instead focus on the likelihood that a parameter of any weather event is likely to exceed or occur in a given area. There are indeed various tools that facilitate the growth and understanding of improving forecast accuracy through probabilistic forecasting methods. Mentioned in a previous article with more detail, ensembles are different iterations with parameters tuned slightly differently to reflect differing outcomes. This approach sacrifices a specific (or range) number in exchange for a probability of occurrence beyond a certain threshold (ex: probability of rainfall total greater than 0.01 inches suggested by the second tweet above). Yes, it may seem tricky given that it’s different than the accustomed way, but it is meant to illustrate the difference and carries an emphasis of its own regard. Agencies like NOAA’s National Severe Storms Laboratory are leading projects such as the Warn-on-Forecast experiment which utilizes sophisticated and refined modeling and data collection techniques to generate weather forecasts based greatly on probability of occurrence. What is the ultimate goal? Forecasters could utilize the added information and produce more accurate forecasts and respond quicker to developing hazards. This in turn could lead to a greater chance of saving life and property in the event of hazardous weather phenomena like tornadoes, flash floods, and large hail by providing ample watch/warning times to the public. It’s safe to say that as forecasting techniques become more refined with time, these two schools of thought will continue to branch out in technicality and carry a bigger impact in their own regard. What do you think about the differences between the two? Would you rather prefer a deterministic forecast with say a total range of rainfall in a given day, or a probabilistic forecast with a message of likelihood that it will rain/storm on a given day or time? Let us know in the comments! Image credit: Tropical Tidbits To learn more about other topics in weather research, be sure to click here! © 2019 Meteorologist Brian Matilla DISCUSSION: According to NASA and NOAA the year 2018 was the fourth warmest on record, coming in at around 0.8 degrees C above the 20th century average. This year also began with La Niña conditions in place over the eastern Tropical Pacific Ocean. These conditions held well into the spring and early summer, then during the fall weak El Niño conditions developed. In spite of the warm year globally, much of the central USA experienced cool conditions in March and April 2018, followed by a warm May and June. Also, a strong cold spell dominated much of the central USA from mid-October through November. The phenomenon described last year, the pool of warm oceanic water in the Northeast Pacific known colloquially as “the blob” (e.g. Bond et al. 2015, Pinhero et al. 2019), was present again this year. But, the “blob” did not make much news in 2018, see last year’s publication for more on this event. Here, we perform an overview the blocking occurrences in 2018 using the University of Missouri blocking event archive (http://weather.missouri.edu/gcc). We will examine the blocking occurrences for each region of the Northern Hemisphere (NH) and Southern Hemisphere (SH) separately, and discuss a few recent trends in blocking activity. a.The Northern Hemisphere As noted in last year’s installment, the number of blocking events that occur annually has been higher since about 2000 than the previous 30-year period (1970- 1999), and a new publication (Lupo et al. 2019) will highlight these trends. During 2018, 50 blocking events occurred over the entire NH, which is higher than last year’s total (40) and quite a bit higher than the mean early 21st century occurrences (38). Since we typically expect +/- 8.5 events, 2018 was a “blocky” year. The persistence of 2018 blocking events was similar to their climatological mean for early 21st century blocks (about 9 days), and their intensity was close to the climatological mean strength as well. Over the Atlantic Region (80 degrees W – 40 degrees E longitude) in 2018, there were 22 blocking events that occurred and this is almost 40% more than the regional mean. We have stated that the occurrence of blocking can be episodic, and during 2018, 13 of these Atlantic Region blocking events occurred over Eastern Europe and Western Russia. Three of these occurred during October and November in particular. The first one during mid-October caused a strong warm spell across much of Eastern Europe and Western Russia (Fig. 1a), and in some places the warmth was record setting. The latter two blocking events occurred during November and were about two weeks in duration each. The first of these was a moderately strong event, while the second was classified as strong. These events led to warmer than normal conditions over northeastern Europe and cooler than normal conditions from Ukraine to the Urals (Fig. 1b) during the month of November. Within the Pacific Region (140 degrees E- 100 degrees W), the 2018 blocking occurrence (13) was close to the climatological normal (12) in number and duration (9-10 days). For the second straight year, most of these blocking events (11) occurred over the Northeast Pacific, but unlike last year, these were distributed throughout the year. One event occurred during mid-October (9-19 October), and combined with the Atlantic Region event described above, resulted in a very cold month for the western 2/3 of North America (Fig. 1a). Thus, North America was caught in the middle of a NH simultaneous blocking episode, which is not exactly rare. However, when the impact North America tend to anchor in persistent cool conditions. Also, Nunes et al. (2017) and references therein show extreme cold over North America is typically associated with blocking in the eastern Pacific Region. As we stated last year, the re-emergence of the Pacific Region “ridiculously resilient ridge” provided the impetus for more Pacific blocking. This also caused more blocking to occur throughout 2018 in the east Pacific. This prevalence for blocking over the eastern Pacific in 2018 led to Alaska experiencing a very warm year as seen in Fig. 1. The early winter saw very little snow over the interior of Alaska.
Figure 1. The Northern Hemisphere surface temperature anomaly (oC) for a) mid-October, 2018 (left), and b) November 2018 (right). In 2018, it was the Continental Region (Weidenmann et al. 2002) that experienced more than double the number of events that during the previous year (2017). This was nearly 50% more than typical as well. These 15 blocking events were sprinkled over the Asian Continent throughout the year, and for the second year in a row, none occurred over North America in 2018. But, as shown in many studies, the occurrence of blocking over North America is comparatively rare. a.The Southern Hemisphere In the SH, there were 27 events during 2018 which is the most since record began to be kept in 1970. This breaks the record previously set in 2013 (24), and this is about a 65% greater frequency of occurrence over the annual climatological value (16.5). Weidenmann et al. (2002) demonstrated that most blocking events occur in the South Pacific and during the months of May and June. The record setting year was paced by the occurrence of 19 events over the South Pacific, and seven over the Indian Ocean sector. Normal for these two regions is 12 and three events, respectively. Like last year, the normal peak time only involved five SH block occurrences (late fall - May and June). Also, following 2017, the spring period from October to December saw six block occurrences. This time of the year is very quiet normally in the SH with respect to blocking activity. Most of the blocking events (16) occurred over the southwest Pacific from Australia to New Zealand and near the dateline throughout the year. This resulted in very warm temperatures over the western Pacific in 2018 (Fig. 2a), and Australian heat was often in the news in late 2018 into early 2019. Additionally Argentina and Brazil were cooler than normal. The SH blocking of 2018 was a little less persistent than typical, the mean event lasting for seven days (compared to eight typically). During the year, only three events persisted for more than 10 days. These were a 17-day event near Australia in October, and two events (10 and 12 days) during the month of May. One of these May events occurred over the western Pacific and the other over the eastern Pacific. This double blocking event resulted in a temperature pattern for a 12 day period that mimicked the year overall in general (Fig. 2). Note than much of South America experienced cooler winter season temperatures at this time. In spite of the increased occurrence of SH blocking in 2018, the intensity of these events was very close to the climatological mean.
Figure 2: The Southern Hemisphere surface temperature anomaly (oC) for a) all of 2018 (left), and b) 24 May – 5 June 2018 (right).
In summary, for the third consecutive year, the number of blocking events globally was up (77 events). During 2018, there were 26% more blocking events globally than in 2017 (61) and this difference was accounted for by positive anomalies in both hemispheres. Only the NH Pacific and SH Atlantic showed blocking occurrences near the climatological norm, all other regions discussed were greater than normal. This year there were not any blocking events occurring in either hemisphere that made it onto the list of the top 20 strongest or persistent blocking events on record. Also, the duration and intensity of blocking in both hemispheres were very consistent with those which have occurred since 2000. Finally, blocking episodes were at least partly responsible for anomalous warm temperature conditions over Eastern Europe (especially the fall), the northeast Pacific and Alaska, and the entire western Pacific from Australia to New Zealand during 2018. Blocking also brought cooler conditions to the central USA and South America during their respective fall seasons, and over western Russia up to the Urals during the fall. References: Bond, N.A., Cronin, M.F., Freeland H, and Mantua, N., 2015: Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophysical Research Letters, 42, 3414-3420. DOI: 10.1002/2105GL063306, 2015. Lupo, A.R., A.D. Jensen, I.I. Mokhov, A.V. Timazhev, T. Eichler, and B. Efe, 2019: Changes in global blocking character during the most recent decades, Under Review, Atmosphere, January, 2019. Pinheiro, M.C., Ullrich, P.A., and Grotjahn, R., 2018” Atmospheric blocking and intercomparison of objective detection methods: Flow field characteristics. Under Review, Climate Dynamics, January, 2019. Wiedenmann, J.M., A.R. Lupo, I.I. Mokhov, and E. Tikhonova, 2002: The Climatology of Blocking Anticyclones for the Northern and Southern Hemisphere: Block Intensity as a Diagnostic. Journal of Climate, 15, 3459-3473. Anthony R Lupo is a professor of Atmospheric Science specializing in the study of blocking anticyclone and jet stream dynamics at the University of Missouri and contributor to The Global Climate and Weather Center. To learn more about other global weather research topics, click here! © 2019 Meteorologist Anthony Lupo  DISCUSSION: In order for cloud droplets to form, they must have something to condense onto in our atmosphere (e.g., grain of dust, sea salt, etc.) Thus, it is thought that aircraft, for example, can aid cloud formation by emitting particles in their exhaust in addition to water vapor. However, scientists in Finland conducted a study where they found a different way for aircraft to potentially enhance precipitation processes. The picture above (image credit: Michael Bryant-Mode) is a dramatic illustration of the potential interaction between aircraft and clouds.
We have to first understand some basic ideas about precipitation formation before understanding the results of the study. When cloud droplets or cloud ice crystals first form, they are too small to fall as precipitation. In a pure liquid or pure frozen cloud, bigger droplets/crystals fall faster than smaller ones, collide with and stick to the smaller droplets/crystals, and eventually become large enough to fall as precipitation. In our atmosphere, water often doesn't freeze at 32 degrees Fahrenheit, but can exist in liquid form down to -40 degrees Fahrenheit (i.e., supercooled water). Thus, clouds can and often do contain a mixture of ice and liquid water. In this situation, the ice grows at the expense of the liquid, and this growth process is often much quicker than if the cloud was pure ice or pure liquid. Imagine there is a cloud of supercooled liquid water (no ice) through which an aircraft flies. As the plane's wings and/or propeller moves through the air, the pressure and density of the air change such that temperature rapidly drops in a small area. This temperature decrease can result in the freezing of some of the supercooled water which can then trigger the accelerated ice growth process described above. These large ice crystals can then fall faster, collide with other smaller ice crystals, and grow even faster. Thus, even if aircraft produced no exhaust, the study from the Finish scientists indicated that 6-14 times more precipitation could be produced over a small area than if no aircraft flew through the cloud. This is an example of another way that human activity can potentially influence the weather, perhaps in a way that we haven't thought of before. To learn more about other topics in weather research, be sure to click here! © 2019 Meteorologist Dr. Ken Leppert II 
DISCUSSION: When it comes to weather forecasting, the first-order explanation involves the following process: Determine the region of interest, select an appropriate dataset, run a numerical model with the dataset to generate outputs, and create a forecast based on the model output. That may be the most straightforward answer but in truth, there is a plethora of information that lies within each individual step that would make this article become excessively long if driven into with such detail. However, as models become more complex and with the advent of supercomputing powerhouses such as Cheyenne (National Center for Atmospheric Research), broaching this topic has many useful insights and applications for a greater public understanding.
The Weather Research and Forecasting (WRF) model is currently the flagship numerical weather prediction engine that is capable of ingesting large quantities of data from a currently-operational model and in turn produces a forecast based on many billions of calculations of the physical and atmospheric governing equations. These model datasets range from a fine-scale, regional model such as the North American Model (NAM, which can use a grid scale of as small as 3km) to a coarser-scale yet fully global-coverage model such as the Global Forecast System (GFS, usually 0.25 degrees). On the time scale, one could utilize a more transient, rapidly-updating model such as the High Resolution Rapid Refresh (HRRR; updates each hour) to the less frequent but more comprehensive data from the European Center for Medium-range Weather Forecasting model (ECMWF; updates every 12 hours). The options are abundant, and through the help of powerful tools that can retrieve and process data from satellites and ground-based data, WRF also has the ability to perform calculations using these extra pieces of information. All in all, the central goal of such a powerful forecasting model is to provide as clear a depiction of the state of the atmosphere in the present and future through the help of weather model data and observations. In many cases, forecasters often derive their outlook from a series of different outcomes known as an ensemble forecast. In general, ensembles are a group of forecasts that use a slightly different set of conditions in order to get a better understanding of the. These changes can range from the way that WRF utilizes a different atmospheric physics scheme to the way that WRF. WRF is re-run multiple times with subtle changes to the initial conditions, and that can yield vastly different forecasts which is where the element of uncertainty in forecasting comes into play. Bear in mind that the atmosphere is a dynamic fluid and changes are consistently occurring, so it is essential to understand and judge the forecasts appropriately. However, power still lies within the forecaster’s skill to interpret the most reasonable forecast given the expected changes in the short-term, and that holds valid when using multiple model products. A great example of this was the most recent “snow squall” that impacted portions of the Mid-Atlantic states of Pennsylvania and New York. The consensus between the 12km NAM and the 14km Community Atmosphere (CAM) model both showed snow in their forecast, but due to inherent differences in the techniques between the two models, the CAM was able to resolve, or show the development of, a squall-like feature in the forecast several hours in advance. It once again highlights the importance of analyzing multiple products to develop a precise forecast, but the availability of such vast options means more potential for forecasters to make sound decisions for short-term weather forecasts. So what’s the future of weather forecasting and forecast models? It’s still a fresh research topic for researchers and forecasters alike and is applicable to many facets of daily and sub-daily forecasting. Model configurations at different space and time scales have potentially differing outcomes and greater computing power and increasingly efficient techniques will give forecasters the knowledge they need to issue more accurate forecasts in the coming years. Will we see a new system supersede WRF in the near-future? Possibly, as change is essential to the improvement of weather forecasting. But the recent improvements are a welcome sign for the near-future. To learn more about other topics in weather research, be sure to click here! © 2019 Meteorologist Brian Matilla  Ice core drilling helps scientists study past climate. Scientists that study past climate are called paleoclimatologists. Some paleoclimatologists drill out cores of ice from glaciers in the north and south poles to determine the temperatures of past years. Drilling out a core of ice shows different layers of snow, ice, and bubbles depending on the temperature of the time that a certain layer was at the surface. NASA explains where these ice cores originate. Ice sheets and glaciers form from accumulating snowfall. Each year a new snowfall accumulation falls on top and compresses the previous year’s snow. Eventually, this compression of snow overtime is what makes a glacier. NASA also states that in some areas these layers result in ice sheets that are several miles thick. According to the American Geosciences Institute (AGI), each ice layer shows the past temperature of the air. Scientists use the bubbles of oxygen molecules in the ice to determine what the temperature was for each layer of ice. Oxygen isn't the only gas that creates bubbles in the ice. Allegra LeGrande of NASA states that “scientists can directly measure the amount of greenhouse gases that were in the atmosphere at that time by sampling these bubbles.” Other gases found in ice cores include carbon dioxide and methane. Aerosols are also found within the ice such as, dust, ash, pollen, sea salts, and other chemicals/toxins. The picture below is an ice core that was drilled out of Mount Hunter, Alaska by Bradley R. Markle as part of the Denali Ice Core Project. The discolored layers of the ice have bedrock and pebbles incorporated into the ice.  Overall, the study and drilling of ice cores help scientists understand how climate and the content of the atmosphere changed over thousands of years.
© 2019 Weather Forecaster Brittany Connelly 
DISCUSSION: All over the world every day, there are a multitude of different factors which go into how various numerical model forecasts get generated. All the way from surface observations, to weather balloons, and on to aircraft data, there is a plethora of atmospheric data which is pumped into weather forecast models to help generate more realistic and near-term and longer-term forecast output. This is a result of the fact that, the more data and the greater the density of various data which being injected into the initial conditions of a numerical forecast model, the more accurate the forecast output will typically be.
The other major data component which is included as part of making projections with output from numerical forecast models which most people do not think about are those data resources which emanate from ocean-going ships. The primary data source which is most commonly referred to as “ship track” has to do with shipping vessels reporting atmospheric state-variables such as temperature, pressure, moisture, wind direction, and wind speed to archiving data systems over land. Moreover, since there is far more ocean on planet Earth than there is land, this leads to there ultimately being a substantial amount of data which comes in from “ship tracks.” As shown in the graphic above (courtesy of Meteorologist Zack Labe from University of California-Irvine), you can clearly see how widely dispersed the shipping tracks were between 2004 and 2005 alone across the Northern Hemisphere. However, in looking to the Southern Hemisphere, you can see how there were substantially fewer shipping tracks archived during that 1-year period. Hence, it goes without saying that there is far much more shipping tracks data which is fed into both regional, synoptic, and global scale numerical forecast models across the Northern Hemisphere. This is also not surprising since most of the people which live on Earth preside within the Northern Hemisphere. Thus, it just goes to show how shipping tracks can tell someone a lot more than “meets the eye.” To learn more about other interesting weather research topics, be sure to click here! © 2018 Meteorologist Jordan Rabinowitz How Have the GOES-East and Himawari-8 Satellites Revolutionized Tropical Cyclone Research?11/2/2018   DISCUSSION: There is no debate whatsoever that meteorological research has come quite a long way over the past 40 to 50 + years since the start of the modern remote sensing era. Having said that, there are still many mysteries concerning various details of the atmosphere and its many phenomena it creates that remain unknown (and/or possibly undetected) to atmospheric scientists around the world. One such example of an atmospheric phenomenon which remains somewhat alluring to atmospheric scientists around the world are gravity waves observed across the cloud-top expanse of intense tropical cyclones.
The primary reason for why gravity waves emanating from the inner cores of intense tropical cyclones have remained mysterious until more recent years (i.e., years since late 2016 when the GOES-16 or GOES-East satellite imager was launched into orbit) is due to the fine-scale at which this phenomenon occurs in real life. More specifically, prior to the years in which the GOES-East satellite imager was in active status, such a fine-scale atmospheric cloud-based phenomenon was nearly impossible to ever observe in real-time and study to any legitimate extent. However, with the advent of the GOES-16 satellite imager as well as its Western Pacific counterpart by way of the Himawari-8 satellite imager, atmospheric scientists have completely changed the game in terms of the high resolution at which atmospheric features can now be studied. In getting to intense tropical cyclone-based gravity wave formation (one such example of which is captured above in association with Super Typhoon Meranti which occurred back in September 2016 over in the Western Pacific Ocean), such gravity waves which effectively look like “ripples in a pond” which emerge from the center of intense tropical cyclones form as a result of intense inner to outer pressure gradients. To be more precise, as a given tropical cyclone intensifies rapidly, there is a corresponding rapid change in pressure from the inner to outer parts of a tropical cyclone. This increasingly rapid change in atmospheric pressure from the center outwards generates a wave-like response which realizes in the form of gravity waves. Thus, this just goes to show how the current state-of-the-art satellite era has changed the way in which we observe Earth’s atmosphere. To learn more about other neat global meteorological research topics from around the world, be sure to click here! © 2018 Meteorologist Jordan Rabinowitz 
DISCUSSION: There is little to no debate throughout the global atmospheric science community that hurricane forecasting has undergone a substantial and noticeable amount of improvement over the last thirty to forty years plus. Both with respect to being able to better anticipate the forward track of developing an intense tropical cyclones to also being able to predict their intensity as well as maximum intensity father in advance, there is no argument that atmospheric science has made tremendous strides in being able to better anticipate critical changes within tropical cyclones. Having said that, over the past ten to twenty years or so, operational hurricane forecasters over the NWS National Hurricane Center have developed ways to make even greater strides than many other regions of the world. This is due to an incredibly intelligent and experienced collective team of research and operational forecasters on hand as well as direct access to state-of-the-art numerical forecast model resources.
More specifically, many of the better tropical cyclone forecasts from around the globe have emerged in the wake of the introduction of ensemble forecast methods. Ensemble forecasting is a mode and an approach to operational forecasting wherein the forecaster in question utilizes a forecast model whose sole job is to effectively modify specific properties of model conditions at the start of the run to create various forecast solutions which add up to what is referred to as an ensemble. By then blending the respective future track and intensity results from the collection of different individual ensemble members, this then facilitates a better idea of the more likely range of possible solutions for the future of a given tropical cyclone event. In the case of the current highest concern across the tropical Atlantic Ocean which happens to be Tropical Storm Florence, operational forecasters have been and will continue to integrate ensemble forecast approaches quite heavily right up to the point of landfall to have the best possible edge on the range of possibilities for the future track and intensity of what will more-than-likely soon again be Hurricane Florence. Moreover, it looking at the graphic attached above (courtesy of Meteorologist Sam Lillo from the University of Oklahoma), you will see how it is noted that the intensity forecast out to 5 days for Tropical Storm Florence happens to be the highest intensity forecast out through a 5-day period over the past twenty years. More importantly, this reflects how overall forecast confidence has evolved and changed over that time-span with the advent of higher-resolution regional as well as global models combined with infused ensemble forecasting techniques as well. To learn more about other interesting topics from across different sectors of meteorological research, be sure to click here! © 2018 Meteorologist Jordan Rabinowitz  In 2016, NASA introduced the Cyclone Global Navigation Satellite System (CYGNSS). Instead of one big satellite in the sky that passes over an area twice a day, the CYGNSS are eight little satellites that work together as a constellation. These eight small satellites are low to Earth’s surface and orbit on a single launch vehicle, recording the ocean surface winds. This project, fully introduced in 2016, was created to gather more information about hurricanes, since there is no other type of instrument to gather information on the ocean so close to the eye of a tropical cyclone, hurricanes or typhoon. Since there are multiple satellites measuring just the tropics, it allows these regions to get a better picture. Each satellite passes over a region every 12 minutes, which produces a new image every few hours, compared to every few days. This fast imaginary allows scientists to get a better understanding of these rapidly changing storms.
The information that is being measured and recorded from the CYGNSSnot only allows scientists to get a better understanding of these storms but allows them to see things that they have never been able to see before. Along with the microwave signal that the CYGNSS uses to detect wind speeds, the CYGNSS also has a built-in Global Positioning Satellite, GPS (the same one used in cars), which reduces the choppiness of the ocean to help determine the wind speeds over the ocean surface. The GPS can also pick up reflections of standing water and the amount of moisture in the soil. Putting all of this information together, scientists can detect floods, a common occurrence during hurricanes. Since hurricanes can have very rapid intensification, the old instruments used to observe them would only produce new information every two to three days, which meant that scientists were missing a lot of information. The CYGNSS allows scientists to receive data every 12 hours. This information can show how quickly an area floods during a hurricane. Since the GPS in the CYGNSS is able to pick up reflections of standing water, scientists can discover flooded areas caused by hurricanes. This new information that is produced from the CYGNSS is groundbreaking. Since it is still new, more and more information is being discovered daily about how powerful this new satellite system really is. Scientists can spot flooding in areas that are hit by hurricanes or by overflow from rivers. Just recently scientists have been able to spot rivers off the Amazon River. Without the CYGNSS, satellites were not capable of capturing these rivers; this is due to the distance between ground and satellite. Being so close to Earth’s surface, they are able to find standing water through clouds and vegetation. The rivers they are able to see coming off the basin of the Amazon river are only hundreds of meters wide. CYGNSS principal investigator, Chris Ruf said: “When I saw the first land images of inland water bodies, I was amazed at their quality.” He continues by saying that the thought of being able to see these types of things in the past just seemed to impossible, but the high-resolution images that the CYGNSS produces are outstanding. While all of this is still really new information, it’s something that will be useful going forward to help predict and assesses storms. To learn more about the latest meteorological research, click here! © 2018 Weather Forecaster Allison Finch   DISCUSSION: With ever-increasing processing power being made available by the world’s top scientific research supercomputers, forecasters and researchers alike are keen to stay on the cutting edge of forecasting accuracy and reliability. The European Centre for Medium-range Weather Forecasting (ECMWF) has recently announced that several new upgrades and improvements are being introduced in the latest release of the ECMWF Integrated Forecasting Package (IFS) cycle 45r1. The IFS uses a sophisticated four-dimensional data assimilation scheme (e.g., latitude-longitude-altitude-time) and is fed information from a combination of observational data and model outputs which leads to generate high-resolution forecasts. Forecasts within the IFS are further separated into two classes: a high resolution forecast and an ensemble-based forecast.
New meteorological content is being introduced in IFS cycle 45r1 that will potentially enhance forecasting skill and quality. One of the biggest improvements in this cycle is the introduction of a three-dimensional coupled ocean-atmosphere scheme with data obtained from the Nucleus of European Modeling of the Ocean (NEMO) dataset version 3.4. Because the ocean and atmosphere work consistently in tandem, coupling of the ocean-atmosphere interface is important when considering accurate simulations of future conditions. The existing NEMO-IFS scheme has also been upgraded to allow for a full ocean-atmosphere coupling in the tropics, with partial coupling in the extratropics. Another significant improvement is with regards to the bathymetry model, which has been upgraded to use the National Oceanic and Atmospheric Administration’s ETOPO1 (1 arc-minute) locked topography-bathymetry dataset. This dataset is a significant improvement from the predecessor dataset in that many biases in estimated bathymetrical depth have been corrected for and many erroneous measurements have been addressed. The importance of this is that the wave model within the IFS can tap into this improved data and forecast accuracy of wave heights can be greatly improved as a result. The figures at the top of this article shows the adjustments to the bathymetry with ETOPO1 data compared to the predecessor dataset in both the high-resolution and ensemble wave models. So what do these improvements mean for us? Recalling the spirit of a coupled ocean-atmosphere interface, many improvements to the upper air forecasts are expected. Understanding more about our upper-air dynamics will provide more clues on predictability of. Near-surface temperature and precipitation biases also receive an improvement on the predecessor cycle, especially in the tropical regions and over Europe. On the tropical cyclone front, intensity error has been decreased by as much as 10% over the first 5 days of a forecast and up to a 20% reduction in error beyond day 5. This is an important topic that is stressed upon global forecasters for hurricane intensity changes, especially since rapid intensification processes in tropical cyclones continue to be a challenge for forecasters and researchers alike. While it is still currently in the open testing phase, these new upgrades are expected to be released in just a few days (June 5th, 2018). For a complete description on the new improvements, additions, and preliminary findings with the new IFS cycle besides those mentioned in this article, check out the ECMWF documentation here. Image credit: European Centre for Medium-range Weather Forecasting To learn more about the latest meteorological research, click here! © 2018 Meteorologist Brian Matilla Discussion: A study recently published in the Bulletin of the American Meteorological Society evaluated the presence, position, and education of women weathercasters in local TV. The purpose of the study was to “determine updated numbers that reflect whether women are gaining more positons and influence in local TV weather broadcasting compared to the past”. This was evaluated by examining if a correlation exists between how many female weathercasters hold meteorological degrees and are chief meteorologist. Finally, it examined if there was a correlation between having a meteorological degree and working in a larger market. Altogether, the data were obtained between February 27 and October 20, 2016, and represented 2,040 weathercasters. Of that total, 1,444 were men and 596 were women. Local TV station personnel and websites across the U.S. provided the data for this study. Additionally, an up-to-date list of local network affiliates and regional cable channels from 210 U.S. markets was compiled via the website NewsBlues. From station websites, weathercaster biographies provided personal information including the level of education, whether or not that individual earned a degree, and their position at the station. Finally, personnel from each station provided more detailed, short biographies on news and weather team members to fill in any informational gaps. Images 1 & 2. Data of weathercaster degrees from the recent study. This study was one of the first to compare the number women and men weathercasters holding meteorology degrees. When polling the educational backgrounds, as seen in image 1, the majority of both male and female degrees were meteorology undergraduate degrees (778 men and 282 women). Interestingly enough, both male and female meteorology degrees were more common in smaller markets (seen in image 2). The next most common educational backgrounds included communication/journalism degrees, professional meteorology certificates such as the American Meteorological Society and National Weather Association Seals of Approval for TV Weathercasting, meteorology master’s degrees, and other science degrees. Images 3-5. Data of the four most common positions by gender from the recent study.  Image 6. Data of chief meteorologists from the recent study. The four most common positions of weathercasting are evening, morning, weekend, and daytime. As seen in images 3 and 5, This study found that most women weathercasters, 44%, worked the least desired and least prestigious time slot, the weekend shift. The next 37% of women worked mornings. There is a huge imbalance in the male to female ratio of evening weathercasters. 45% of men weathercasters hold this prime-time shift (images 4 and 5) while only 14% of women weathercasters work evenings (image 3). This percentage was actually lower than a previous study in 2008 that found nearly a third of women weathercasters worked in the evening/prime-time shift. Similarly, out of all chief meteorologists, only 8% are female (image 6).
Overall, this study found that the total percentage of women weathercasters in local TV has increased. Even so, women are underrepresented in the field as they mainly work undesired weekend shifts. Much fewer women than men have meteorological degrees, hold evening positions, and hold high-ranking positions including chief meteorologist. Finally, it may be useful to explore additional contributing factors for to further comprehend the results of this study. To learn about the latest meteorological research, click here. © 2018 Weather Forecaster Amber Liggett Could There Eventually Be a Revised Hurricane Intensity Scale? (credit: American Geophysical Union)2/9/2018  DISCUSSION: In light of a gradually warming planet, there is a globally increasing concern that there could be an issue related to gradually increasing tropical cyclone intensity. This is chiefly due to the fact that as the Earth continues to experience amplified net warming over time due to an increasingly more amplified greenhouse effect, this will consequently catalyze greater global oceanic warming. The reason for this is due to the fact that well over half (50%) of the world's heat is stored in the world's oceans. Therefore, with warmer ocean's, this results in a corresponding increase in the magnitude of warmer upper-ocean heat energy which is made available on a seasonal basis to developing tropical storms.
Therefore, one of the growing concerns is that (even with all other atmospheric factors being equal such as the Coriolis force which helps dictate at what latitudinal positions tropical storms can form at) with a gradually warming planet, there would be increasing amounts of low/mid-level water vapor present. Thus, with warmer oceans, there is an inherently greater threat for potentially stronger tropical cyclones in the coming years and decades to come. Hence, it will be interesting to see if atmospheric researchers eventually make a more conscious effort to look into whether it would be advantageous to establish a slightly different (possibly with an increased intensity category) hurricane intensity scale to compensate for these factors. For the time being, the global atmospheric science community is in fairly solid agreement that the current Saffir-Simpson Hurricane Wind Intensity Scale will likely continue to be the way to go as it has worked for the global scientific and non-scientific communities alike up to this point. To learn more about the article which inspired this article, click here! To learn more about other interesting research topics both directly and indirectly connected to atmospheric science, be sure to click here! © 2018 Meteorologist Jordan Rabinowitz  DISCUSSION: “People generally want to know three things about any hurricane: when and where it will make landfall, and how bad will it be.” To be able to forecast and provide the public with all this information, it takes a lot of people, time, and effort to collect the data needed. This past hurricane season was ranked the fifth-most active season since records began in 1851, with 17 named storms. Regarding research during the last hurricane season, the National Hurricane Center (NHC) issued seven rapid intensification forecasts, and of which, six were correct. Periods of rapid intensification indicate that the maximum sustained winds associated with a tropical cyclone have increased by at least 30 knots (35 mph), or above, within a 24-hour period. But what is it that makes the associated hurricane research so important?
The amount of information and research on just one single hurricane can be an overwhelming amount, making the amount of information on all hurricanes mind boggling. It is this information that is collected and referenced both during and after a given tropical storm. This allows the National Oceanic and Atmospheric Administration as well as the National Hurricane Center to create models and forecasts for the public. To understand how various hurricane research protects the public, you must understand the basics of a hurricane. Most hurricanes which form within the Tropical Atlantic basin develop within the Caribbean Sea and/or the North Atlantic Ocean. Often times, the most destructive tropical cyclones form off the coast of western Africa when thunderstorms travel westward and gradually develop an area of lower pressure near the center of the predominant convection. The localized change in minimum central pressure within the developing tropical storm catalyzes an increase in the inward rotation of the wind flow towards the center of the developing circulation. While traveling across the warm waters over the course of what is most often several days, these storms can become very dangerous. The National Oceanic and Atmospheric Administration and NASA can collect data on these hurricanes from a combination of both satellites and aircraft. They collect information about the rainfall rates, surface wind speeds, cloud heights, environmental temperature, ocean heat, and humidity. Each of these things effect how the storm is going to evolve and how it may ultimately end up impacting people living in given regions being threatened by said storm. One of the instruments that is deployed from aircraft(s) that fly into hurricanes is referred to as a Dropsonde. According to NASA, a Dropsonde is an 11-inch long tube that is light and flimsy. It includes a parachute to slow it down and is ejected from one such un-manned aerial vehicle which is known as the Global Hawk. While it falls, it both measures and collects formation about vertical profiles of temperature, humidity, as well as both wind speed and direction. Upon collecting this critical information, the dropsonde immediately transmits the information back to a computer. One of the most important measurements is the wind speed. This is due to the fact that upon a hurricane hitting land, the storm surge is a direct result of how the strong the winds are. Without being able to predict strong winds in advance, affected areas can’t prepare and evacuate accordingly. The storm surge flooding can often generate life-threatening situations when not forecasted properly. Back in the day when there was a major lack of geostationary satellites and aircraft to help forecast such events, hurricanes were a much greater threat to society due to the greater lack of a more accurate predictability factor. As someone may infer, scientific research has made substantial progress in how we forecast hurricanes. However, it is crucial to continue researching/learning more about hurricanes. (Citied: NASA, National Weather Service, NOAA, Hurricane Hunters Association) To learn more about other interesting topics in meteorological research, be sure to click here! © 2018 Weather Forecaster Allison Finch  
DISCUSSION: As the research world wraps up neat global atmospheric events from throughout the course of 2017, one of the more interesting subjects to review is the importance of atmospheric blocking events. There is no debate that atmospheric blocking events have a profound impact on the larger-scale atmospheric flow regimes which evolve both on the synoptic-scale (i.e., spatial coverage on the order of thousands of kilometers) and the planetary scale (i.e., a spatial coverage on the order of hundreds of thousands of kilometers). Therefore, both direct and indirect impacts of atmospheric blocking events can often have incredibly far-ranging impacts on regional as well as continental weather events (and trends thereof). Attached above is a neat discussion (courtesy of Dr. Anthony Lupo of the University of Missouri) which helps to break down the duration of 2017 in the context of atmospheric blocking events and corresponding issues therein.
To learn more about other interesting topics in atmospheric research, be sure to click here! © 2018 Meteorologist Anthony Lupo Matt Bolton (my intern graduate and now professional colleague) and I have been discussing, for years, public understanding of weather. The discussion grew out of our hurricane and other research efforts, pre-college-level weather camp programs, and interactions with social scientists at professional weather conferences…Toward this end, Matt has just posted a survey (Fig. 1) to further his research…To read the full story, click here - http://www.weatherworks.com/lifelong-learning-blog/?p=1438 © 2017 H. Michael Mogil  |
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