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The GOES-R Lightning Mapper Sensor

The Lightning Mapper Sensor on GOES-R builds on previous measurements of lightning from low earth orbit by the OTD (Optical Transient Detector) and LIS (Lightning Imaging Sensor) sensors. Unlike observations from low earth orbit, the GOES-R platform will allow continuous monitoring of lightning activity over the Continental United States and southern Canada, Central and South America, and portions of the Atlantic and Pacific Oceans. The LMS will detect total (cloud-to-ground and intracloud) lightning at storm scale resolution (approx. 8 km) using a highly sensitive Charge Coupled Device (CCD) detector array. Discrimination between lightning optical transients and a bright sunlit background scene is accomplished by employing spectral, spatial, and temporal filtering along with a background subtraction technique. The result is 24 hour detection capability of total lightning. These total lightning observations can be made available to users within about 20 seconds. Research indicates a number of ways that total lightning observations from LMS could benefit operational activities, including 1) potential increases in lead times and reduced false alarms for severe thunderstorm and tornado Warnings, 2) improved routing of &rail around thunderstorms, 3) support for spacecraft launches and landings, 4) improved ability to monitor tropical cyclone intensity, 5) ability to monitor thunderstorm intensification/weakening during radar outages or where radar coverage is poor, 6) better identification of deep convection for the initialization of numerical prediction models, 7) improved forest fire forecasts, 8) identification of convective initiation, 9) identification of heavy convective snowfall, and 10) enhanced temporal resolution of storm evolution (1 minute) than is available from radar observations. Total lightning data has been used in an operational environment since July 2003 at the Huntsville, Alabama National Weather Service office. Total lightning measurements are obtained by the North Alabama Lightning Mapping Array (LMA) and have successfully been used in warning decisions. Every 2 minutes, total lightning counts in 2 km by 2 km horizontal, 1 km vertical grids are available to forecasters on an AWIPS (Advanced Weather Interactive Processing System) workstation. Experience with the LMA total lightning data is used to illustrate the potential use of LMS data that would be available to forecasters across the US. This abstract is for submission as a presentation to the National Weather Association Annual Meeting to be held 16-21 October 2004 in Portland, OR. This abstract will be published in the conference proceedings.

Buechler, Dennis↗

Relationship of Global Precipitation Measurement (GPM) Mission to Global Change Research

In late 2001, the Global Precipitation Measurement (GPM) mission was approved as a new start by the National Aeronautics and Space Administration (NASA). This new mission is motivated by a number of scientific questions that are posed over a range of space and time scales that generally fall within the discipline of the global water and energy cycle (GWEC). Recognizing that satellite rainfall datasets are now a foremost tool for understanding global climate variability out to decadal scales and beyond, for improving weather forecasting, and for producing better predictions of hydrometeorological processes including short-term hazardous flooding and seasonal fresh water resources assessment, a comprehensive and internationally sanctioned global measuring strategy has led to the GPM mission. The GPM mission plans to expand the scope of rainfall measurement through use of a multi-member satellite constellation that will be contributed by a number of world nations. This talk overviews the GPM scientific research program that has been fostered within NASA, then focuses on scientific progress that is being made in various research areas in the course of the mission formulation phase that are of interest to the global change scientific community. This latter part of the talk addresses research issues that have become central to the GPM science implementation plan concerning: (1) the rate of global water cycling through the atmosphere and surface and the relationship of precipitation variability to the sustained rate of the water cycle; (2) the relationship between climate change and cloud macrophysical- microphysical processes; and (3) the general improvement in measuring precipitation at the fundamental microphysical level that will take place during the GPM era and an explanation of how these improvements are expected to come about.

Smith, Eric A.↗

Mesoscale Variability in SUCCESS Data

Analysis of meteorological, chemical and microphysical data from the airborne SUCCESS (SUbsonic aircraft Contrail and Cloud Effects Special Study) mission is reported. Careful analysis of the complex DC-8 flight pattern of May 2, 1996 reveals 19 linear flight segments within six main geographical areas, which we have analyzed. Significant mountain wave activity is revealed in the data from the MMS (Meteorology Measurement System) and MTP (Microwave Temperature Profiler) instruments on the DC-8, which resembles previous observations of mountain wave structures near Boulder, Colorado. Strong mountain-wave-induced upwelling downwind of the Rockies is noted. Turbulence is also noted in regions of the mountain wave consistent with overturning near the tropopause. Zonal winds recorded on the ER-2 are shown to be consistent with mountain wave breaking at or near critical levels in the stratosphere, consistent with the strong turbulence reported by the pilot during the ER-2 flight. These observations have been supported with spectral analyses and modeling studies. 'Postcasts' of mountain wave activity on May 2, 1996 using the Naval Research Laboratory Mountain Wave Forecast Model predicts both strong mountain wave activity near the tropopause and strong mountain-wave-induced turbulence in the stratosphere.

Eckermann, Stephen D.↗

Airborne Lidar Measurements of XCO 2 in Synoptically Active Environment and Associated Comparisons With Numerical Simulations

Frontal boundaries have been shown to cause large changes in CO 2 mole-fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO 2 dry air mole-fraction (XCO 2 ) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO 2 frontal contrasts (ΔXCO 2 , i.e., warm minus cold sector average of XCO 2 ). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO 2 during four seasonal field campaigns of the Atmospheric Carbon and Transport-America (ACT-America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO 2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO 2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting-—Chemistry (WRF-Chem) showed that (a) all products had a similar sign of ΔXCO 2 though with different levels of agreement in ΔXCO 2 magnitudes among seasons; (b) ΔXCO 2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO 2 frontal contrasts. A linear regression analyses between ΔXCO 2 for MFLL versus GMAO, and MFLL versus WRF-Chem for summer-2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO 2 variability among four seasons provide guidance to the spatial structures of XCO 2 transport errors in models and satellite measurements of XCO 2 in synoptically-active weather systems.

54 ENVIRONMENTAL SCIENCES↗

A Scheme for finding the Front Boundary of an Interplanetary Magnetic Cloud

We developed a scheme for finding the front boundary of an interplanetary magnetic cloud (MC) based on criteria that depend on the possible existence of any one or all of six specific solar wind features. The features that the program looks for, within +/- 2 hours of a preliminarily determined time for the front boundary, estimated either by visual inspection or by an automatic MC identification scheme, are: (1) a sufficiently large directional discontinuity in the interplanetary magnetic field (IMF), (2) existence of a magnetic hole, (3) a significant proton plasma beta drop, (4) a significant proton temperature drop, (5) a marked increase in the IMF's intensity, and (6) a significant decrease in a normalized root-mean-square deviation (RMS)of the magnetic field - where the scheme was tested using 5, 10, 15, and 20 minute averages of the relevant physical quantities, in order to find the optimum average (and RMS) to use. Other criteria, besides these six, were examined and dismissed as not reliable, e.g., plasma speed. The scheme was developed specifically for aiding in forecasting the strength and timing of a geomagnetic storm due to the passage of an interplanetary MC in real-time, but can be used in post ground-data collection for imposition of consistency in choosing a MC's front boundary. The scheme has been extensively tested, first using 80 bona fide MCs over about 9 years of WIND data, and also for 121 MC-like structures as defined by a program that automatically identifies such structures over the same period. Optimum limits for various parameters in the scheme were found by statistical studies of the WIND MCs. The resulting limits can be user-adjusted for other data sets, if desired. Final testing of the 80 MCs showed that for 50 percent of the events the boundary estimates occurred within +/-10 minutes of visually determined times, 80 percent occurred within +/-30 minutes, and 91 percent occur within +/-60 minutes, and three or more individual boundary tests were passed for 88 percent of the total MCs. The scheme and its testing will be described.

Lepping, Ronald P.↗

WRF Modelling of Deep Convection and Hail for Wind Power Applications

Deep convection and the related occurrence of hail, intense precipitation and wind gusts represent a hazard to a range of energy infrastructure including wind turbine blades. Wind turbine blade leading edge erosion (LEE) is caused by the impact of falling hydrometeors onto rotating wind turbine blades. It is a major source of wind turbine maintenance costs and energy losses from wind farms. In the United States Southern Great Plains (SGP), where there is widespread wind energy development, deep convection and hail events are common, increasing the potential for precipitation-driven LEE. A 25-day Weather Research and Forecasting (WRF) model simulation conducted at convection-permitting resolution and using a detailed microphysics scheme is carried out for the SGP to evaluate the effectiveness in modelling the wind and precipitation conditions relevant to LEE potential. WRF output for these properties is evaluated using radar observations of precipitation (including hail) and reflectivity, in situ wind speed measurements and wind power generation. This research demonstrates some skill for the primary drivers of LEE. Wind speeds, rainfall rates and precipitation totals show good agreement with observations. Here, the occurrence of precipitation during power producing wind speeds is also shown to exhibit fidelity. Hail events frequently occur during periods when wind turbines are rotating and are especially important to LEE in the SGP. The presence of hail is modelled with a mean proportion correct of 0.77 and an odds ratio 4.55. Further research is needed to demonstrate sufficient model performance to be actionable for the wind energy industry and there is evidence for positive model bias in cloud reflectivity.

17 WIND ENERGY↗

An Anomaly Correlation Skill Score for the Evaluation of the Performance of Hyperspectral Infrared Sounders

With the availability of very accurate forecasts, the metric of accuracy alone for the evaluation of the performance of a retrieval system can produce misleading results. A useful characterization of the quality of a retrieval system and its potential to contribute to an improved weather forecast is its skill, which we define as the ability to make retrievals of geophysical parameters which are closer to the truth than the six hour forecast, when the truth differs significantly from the forecast. We illustrate retrieval skill using one day of AMSU and AIRS data with three different retrieval algorithms, which result in retrievals for more than 90% of the potential retrievals under clear and cloudy conditions. Two of the three algorithms have better than 1 K rms "RAOB quality" accuracy on the troposphere, but only one has skill between 900 and 100 mb. AIRS was launched on the EOS Aqua spacecraft in May 2002 into a 705 km polar sun-synchronous orbit with accurately maintained 1:30 PM ascending node. Essentially uninterrupted data are freely available since September 2002.

retrieval↗

A User-Focused Renovation of CERES Metadata

Production software and public data products for Clouds and the Earth’s Radiant Energy System (CERES) continue to evolve as the project extends its climate data record. The data management team for CERES is currently undertaking major renovations of both code and data products, the latter of which is, of course, in service of improving user experience. A major mode of CERES’ data product improvement is in renovating products’ metadata. Metadata standards have evolved since CERES began producing its data products in 2000. In its twentieth year, CERES essentially asked the question: how would the project design its data products if it could start all over again? With forthcoming editions, this rebirth will be realized. CERES has redesigned its metadata standards to best position itself for data discoverability. The project has used the latest standards being developed in NASA’s Earth Science Data and Information Systems (ESDIS) Project’s Unified Metadata Model (UMM) documentation; collaborated with the Atmospheric Science Data Center (ASDC) to ensure compliance with Common Metadata Repository compatibility, and continued compliance with Climate and Forecast (CF) Conventions. In doing so, the team created its own, internal document for proper metadata creation and metadata verification software that is deployed prior to all code deliveries. This presentation will discuss this redesign process, as well as needs met and those that are still outstanding in the search for an improved user experience with CERES data products.

Kathleen Dejwakh↗

Regional Impacts of Ice Sheet Surface Representation Improvements in an Earth System Model

Ice sheet surface conditions and their subsequent impact on surface mass balance play an important role in ice sheet dynamics and ice sheet interaction with the overlying atmosphere and surrounding ocean. The NASA Global Modeling and Assimilation Office’s (GMAO) Goddard Earth Observing System (GEOS) ecosystem of models and reanalyses model ice sheet and glacier surface mass balance with a moderately complex snow and ice module, including prognostic surface albedo evolution, fractional snow cover, and snowpack hydrology and meltwater retention. With this configuration, Greenland near surface air temperatures and ice sheet melt are well represented in MERRA-2, GMAO’s current atmospheric reanalysis; however, the similarity to observations in some regions is due, in part, to compensating biases in surface energy budget. Recent improvements to GEOS’ cloud microphysics and longwave radiation parameterizations reduced biases in surface net longwave radiation and exacerbated surface net shortwave radiation biases, effectively increasing near surface temperatures and melting, particularly during the summer months. Here, we demonstrate the Earth system response to an improved representation of the ice sheet surface focused on minimizing the surface energy biases and increasing spatial and temporal representativeness. New changes to the surface albedo parameterization, including the introduction of a prognostic aerosol darkening scheme, help mitigate shortwave biases, while modifications to surface roughness characteristics improve near surface sensible heat fluxes. Focusing on the Arctic and Greenland, we examine the combined impact on these changes on regional weather conditions and ice sheet surface mass balance using a high resolution atmospheric only experiment, and we use a coupled model configuration to assess the impact on oceanic conditions and subseasonal-to-seasonal prediction. Overall, these improvements increase modeled ice sheet surface realism and provide a strong basis for more accurate ice sheet mass balance in GMAO’s future reanalyses and forecasting systems.

Lauren C Andrews↗

Regional Impacts of Ice Sheet Surface Representation Improvements in an Earth System Model

Ice sheet surface conditions and their subsequent impact on surface mass balance play an important role in ice sheet dynamics and ice sheet interaction with the overlying atmosphere and surrounding ocean. The NASA Global Modeling and Assimilation Office’s (GMAO) Goddard Earth Observing System (GEOS) ecosystem of models and reanalyses model ice sheet and glacier surface mass balance with a moderately complex snow and ice module, including prognostic surface albedo evolution, fractional snow cover, and snowpack hydrology and meltwater retention. With this configuration, Greenland near surface air temperatures and ice sheet melt are well represented in MERRA-2, GMAO’s current atmospheric reanalysis; however, the similarity to observations in some regions is due, in part, to compensating biases in surface energy budget. Recent improvements to GEOS’ cloud microphysics and longwave radiation parameterizations reduced biases in surface net longwave radiation and exacerbated surface net shortwave radiation biases, effectively increasing near surface temperatures and melting, particularly during the summer months. Here, we demonstrate the Earth system response to an improved representation of the ice sheet surface focused on minimizing the surface energy biases and increasing spatial and temporal representativeness. New changes to the surface albedo parameterization, including the introduction of a prognostic aerosol darkening scheme, help mitigate shortwave biases, while modifications to surface roughness characteristics improve near surface sensible heat fluxes. Focusing on the Arctic and Greenland, we examine the combined impact on these changes on regional weather conditions and ice sheet surface mass balance using a high resolution atmospheric only experiment, and we use a coupled model configuration to assess the impact on oceanic conditions and subseasonal-to-seasonal prediction. Overall, these improvements increase modeled ice sheet surface realism and provide a strong basis for more accurate ice sheet mass balance in GMAO’s future reanalyses and forecasting systems.

Lauren C. Andrews↗

Inter-Comparison of Lightning Trends from Ground-Based Networks During Severe Weather: Applications Toward GLM

The planned GOES-R Geostationary Lightning Mapper (GLM) will provide total lightning data on the location and intensity of thunderstorms over a hemispheric spatial domain. Ongoing GOES-R research activities are demonstrating the utility of total flash rate trends for enhancing forecasting skill of severe storms. To date, GLM total lightning proxy trends have been well served by ground-based VHF systems such as the Northern Alabama Lightning Mapping Array (NALMA). The NALMA (and other similar networks in Washington DC and Oklahoma) provide high detection efficiency (> 90%) and location accuracy (< 1 km) observations of total lightning within about 150 km from network center. To expand GLM proxy applications for high impact convective weather (e.g., severe, aviation hazards), it is desirable to investigate the utility of additional sources of continuous lightning that can serve as suitable GLM proxy over large spatial scales (order 100 s to 1000 km or more), including typically data denied regions such as the oceans. Potential sources of GLM proxy include ground-based long-range (regional or global) VLF/LF lightning networks such as the relatively new Vaisala Global Lightning Dataset (GLD360) and Weatherbug Total Lightning Network (WTLN). Before using these data in GLM research applications, it is necessary to compare them with LMAs and well-quantified cloud-to-ground (CG) lightning networks, such as Vaisala s National Lightning Detection Network (NLDN), for assessment of total and CG lightning location accuracy, detection efficiency and flash rate trends. Preliminary inter-comparisons from these lightning networks during selected severe weather events will be presented and their implications discussed.

Carey, Lawrence D.↗

Radar Retrieval Evaluation and Investigation of Dendritic Growth Layer Polarimetric Signatures in a Winter Storm

Abstract This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed K dp and Z DR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using Z DR and K dp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals. significance statement Developing snow is often detectable using weather radars. Meteorologists combine these radar measurements with mathematical equations to study how snow forms in order to determine how much snow will fall. This study evaluates current methods for estimating the total number and mass, sizes, and shapes of snowflakes from radar using images of individual snowflakes taken during two aircraft legs. Radar estimates of snowflake properties were most consistent with aircraft data inside regions with prominent radar signatures. However, radar estimates of snowflake shapes were not consistent with observed shapes estimated from the snowflake images. Although additional research is needed, these results bolster understanding of snow-growth physics and uncertainties between radar measurements and snow production that can improve future snowfall forecasting.

Meteorology & Atmospheric Sciences↗

Assessing the Needs of NASA's Near Real-Time Earth Observation Products

"The 2017-2027 Decadal Survey for Earth Science and Applications from Space stated that NASA's Earth Science with planned implementation of applications provides sustained earth observations for societal benefits [1]. The Decadal Survey indicated that data latency is invaluable for time-sensitive applications including disaster risk reduction, wildland fire carbon emissions quantification, real-time measurements of the state of the hydrologic systems and many more. Data latency refers to the time between earth observation and data products available to users. During the past 13 years, NASA's Land, Atmosphere Near Real-Time Capability for Earth Observing Systems (LANCE) continues to provide free access to earth observation products that are made available much quicker than routine processing allows. The latency of most LANCE data products is Near Real-time (NRT) which is defined as less than three hours from satellite observations [2]. LANCE is managed by the Earth Science Data and Information System (ESDIS) Project at NASA Goddard Space Flight Center [3], and a User Working Group (UWG) is responsible for providing guidance to LANCE. LANCE data are used by direct users and brokers who add value to the data [4]. NASA Earth Applied Sciences Program (ASP) is one of the primary users of LANCE, which collaborates with partner organizations and provides support to scientists to solve problems in applications of earth observations. ASP promotes the use of LANCE NRT data products to demonstrate applications in decision making, facilitates end-user feedback to the science team to improve data products, and provides information on future demands for research. LANCE supports applications that need a rapid response including detecting wildland fires and volcanic eruptions, tracking smoke, ash and dust plumes, monitoring air quality and tracking extreme weather events such as hurricanes, landslides, and floods. To gather feedback regarding the availability, accessibility and actionability of NASA's NRT data products for societal benefit, three surveys and a few discussions with experts involved in the topic within ASP were conducted from the perspective of users. Feedback has been collected from users who are interested in using low latency NASA data within application communities of agriculture, disasters, water resources, health and air quality, ecological conservation, wildland fires and capacity building. Analysis-ready NRT data products in a variety of formats have been mentioned many times in the collected feedback, especially for applied users with little to no experience using research-grade earth observation products. Users prefer to have products that can be easily integrated into their existing workflows and take their analysis to the data. HDF5 is a commonly used data format for research, but typically requires some conversion to a more friendly format for applications and regular use in decision-making. Users prefer the GeoTIFF data format that can be directly ingested into a GIS mapping software and platform for data analysis and visualization. For example, LANCE’s fire, flood, SO2 and Black Marble Nighttime Blue/Yellow Composite data products have been integrated into NASA Disasters Mapping Portal, which is an GIS-based open data portal, for users in the disaster management community. There are 291 LANCE NRT layers available through GIBS and Worldview, where users can download a snapshot in GeoTIFF format. Operational users expect data to be processed as close to the user as possible. The collected feedback indicates that LANCE fire products within 3 hours latency would meet the needs of the wildland fire community. The ideal latency for volcanic application is 10-15 minutes. Users in Volcanic Ash Advisory Centers (VAAC) reported that the first forecast volcanic product should be issued within 75 minutes from the volcano eruption [5]. Overall, for disaster applications, data latency within 3 hours is useful while latency greater than 12 hours is not timely enough for operational use. Capacity building and training are critical for users to be able to access, interpret and use data products and tools for their decision making, especially for applied users with limited experience using earth observation products. LANCE data products have been used in a number of capacity building projects domestically and internationally [6]. As LANCE continues to bring new products into the system, users request training to utilize LANCE new and upcoming data products and capabilities in their applications. Due to the limitation of bandwidth and downstream flow paths, users in some developing countries need tools to select and download data for a specific area of interest instead of bulk downloads. The collected feedback also shows the lack of available SAR satellite low latency data products. The advantages of SAR to monitor conditions and changes on the ground through darkness, clouds, volcanic ash, and other atmospheric conditions, are appealing to low latency users. For example, terabytes of low latency but cloudy optical images are not helpful in rapidly identifying the extent of flood or fire impacts. LANCE could be complemented with low latency measurements via the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission [7]. Requests for higher spatial resolution products are expressed. A user from the wildland fire management community reported that products with 30-m spatial resolution could be used to detect small fires. The 30-m Landsat OLI fire data is now part of NASA’s Fire Information for Resource Management System (FIRMS) US/Canada [8]. Within the open and free NASA resources, LANCE disseminates NRT data products in a manner that allows them to be accessible and understandable to both scientific and applied users. In many application areas, latency plays an important or even decisive role where low latency earth observations help people to observe areas of interest, detect and track changes in the environment and make timely decisions. NASA’s Earth Applied Sciences Program promotes the use of LANCE NRT products and builds a bridge between application users and research teams. The collected feedback indicates data latency within 3 hours is useful for most of the applications, and shows the needs of user-friendly, analysis-ready products, and requests training on LANCE’s new and upcoming data products. User feedback has been provided to LANCE UWG for guidance and recommendations, and for translating findings into something actionable.

Tian Yao↗

Comparison of Observations and Predictions of Daytime Planetary-Boundary-Layer Heights and Surface Meteorological Variables in the Columbia River Gorge and Basin During the Second Wind Forecast Improvement Project

The second Wind Forecast Improvement Project (WFIP2) is an 18-month field campaign in the Pacific Northwest U.S.A., whose goal is to improve the accuracy of numerical-weather-prediction forecasts in complex terrain. The WFIP2 campaign involved the deployment of a large suite of in situ and remote sensing instrumentation, including eight 915-MHz wind-profiling radars, and surface meteorological stations. The evolution and annual variability of the daytime convective planetary-boundary-layer (PBL) height is investigated using the wind-profiling radars. Three models with different horizontal grid spacing are evaluated: the Rapid Refresh, the High-Resolution Rapid Refresh, and its nested version. The results are used to assess errors in the prediction of PBL height within the experimental and control versions of the models, with the experimental versions including changes and additions to the model parametrizations developed during the field campaign, and the control version using the parametrizations present in the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction operational version of the models at the start of the project. Results show that the high-resolution models outperform the low-resolution versions, the experimental versions perform better compared with the control versions, model PBL height estimations are more accurate on cloud-free days, and model estimates of the PBL height growth rate are more accurate than model estimates of the rate of decay. Finally, using surface sensors, we assess surface meteorological variables, finding improved surface irradiance and, to a lesser extent, improved 2-m temperature in the experimental version of the model.

54 ENVIRONMENTAL SCIENCES↗

Puerto Rico Historical Climate Analysis: A closer look at complex tropical terrain

Small tropical islands like Puerto Rico are especially vulnerable to climate change impacts, yet are often underrepresented in most datasets because the spatial resolution is too coarse to sufficiently cover their complex terrain. This study was done to better understand how the climate has changed in the various regions of Puerto Rico, which will support a transition to renewable energy and aid in projecting future climate impacts on the residents and land they live on. This study used multiple datasets including Daymet, a daily surface weather and climatological summary; European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5); and station observations. The climate variables examined here include minimum and maximum near-surface air temperature, daily precipitation, incident shortwave radiation, and large-scale upper atmosphere conditions. The time periods cover the years 1950–2019. Results in both Daymet and station observations show long-term warming trends throughout every season. In particular, the minimum winter temperatures increased the most compared with maximum and mean air temperature. Northeast Puerto Rico, where the majority of the island’s population is located, experienced the largest warming. Summertime precipitation has decreased over time, and spring and fall precipitation has increased. The largest increase in fall is because there has been more available precipitable water, lower sea-level pressure, and favorable cyclonic circulations from the trade winds over the past seven decades. Overall, wet days are getting wetter, while dry days are getting drier. While there are good solar energy potential, we see a long-term decreasing trend in shortwave radiation over time in all seasons across entire island, possibly due to the increase of cloud cover.

54 ENVIRONMENTAL SCIENCES↗

The Use of the North Alabama Lightning Mapping Array (NALMA) in the Real-Time Operational Warning Environment During the March 2nd, 2012 Severe Weather Outbreak in Northern Alabama

The North Alabama Lightning Mapping Array (NALMA) is a three-dimensional very high frequency (VHF) detection network consisting of 11 sensors spread across north central Alabama and two sensors located in the Atlanta, Georgia region. The primary advantage of this network is that it detects total lightning, or the combination of both cloud-to-ground and intra-cloud lightning, instead of cloud-to-ground lightning alone. This helps to build a complete picture of storm evolution and development, and can serve as a proxy for storm updraft strength, particularly since intra-cloud lightning makes up the majority of all lightning in a typical thunderstorm. While the NALMA data do not directly indicate severe weather, they can indirectly indicate when a storm is strengthening (weakening) due to increases (decreases) in updraft strength, as the updraft is responsible for charging mechanisms within the storm. Data output are VHF radiation sources, which are produced during lightning breakdown processes. These sources are made into 2x2 km source density grids and are ported into the Advanced Weather Interactive Processing System (AWIPS) for National Weather Service (NWS) offices in Huntsville, AL, Nashville, TN, Morristown, TN, and Birmingham, AL, in near real-time. An increase in sources, or source densities, correlates to increased lightning activity and trends in updraft magnitude as long as the storm is within about 125 km of the center of the LMA network. Operationally, these data have been used at the Huntsville NWS office since early 2003 through a collaborative effort with NASA s Short-term Prediction Research and Transition (SPoRT) Center. Since then, total lightning observations have become an essential tool for forecasters during real-time warning operations. One of the operational advantages of the NALMA is the two-minute temporal resolution of the data. This provides forecasters with two to three updates during a typical volume scan of the WSR-88D radar.

White, Kristopher↗

An Automatic Light Rain Detection Algorithm on NASA MPLNET Lidar Observations in the Frame of WMO GALION Project

The water cycle strongly influences life on Earth. In particular, the precipitation modifies the atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially low-intensity precipitation) at global scale, bedsides improving our understanding of the hydrological cycle, it is crucial to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios, i.e. to apply fast mitigation strategies. In this study we developed an algorithm to automatically detect precipitation from lidar measurements obtained by the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) permanent observational site in Goddard. The algorithm, once full operational, will deliver in Near Real Time (latency 1.5h) a new rain mask product that will be publicly available on MPLNET website as part of the new Version 3 Level 1.5 data. The methodology, based on an image processing technique, can detect only light precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the lidar volume depolarization ratio range corrected composite images, i.e. heavy rain events are unusable as the lidar signal is completely extinguished after few meters in the precipitation or no signal detected because of the water accumulated on the receiver optics. Results from the algorithm, besides filling a gap in precipitation and virga detection by radars, are of particular interest for the scientific community because will help to better understand long-term aerosol-cloud interactions and aerosol atmospheric removal (scavenging effect) by rain as multi-year database being available for several MPLNET permanent observational sites across the globe. Moreover, we developed the automatic algorithm at Universitat Politecnica de Catalunya (UPC) Barcelona, the unique permanent observation station member of MPLNET and the European Aerosol Lidar Network (EARLINET) In the future the algorithm can be then easily applied to any other lidar and/or ceilometer network infrastructure in the frame of World Meteorological Organization (WMO) Global Aerosol Watch (GAW) aerosol lidar observation network (GALION)

Simone Lolli↗

Application of Atmospheric Infrared Sounder (AIRS) Data to Climate Research

The application of hyper spectral radiometric data to climate research requires very high absolute radiometric accuracy and stability. We use cloud-free tropical ocean data from the Atmospheric InfraRed Sounder (AIR) Calibration Data Subset (ADCS) to show that the radiometric precision and stability required climate applications has been achieved. The sea surface skin temperatures derived from the AIRS 2616cm-1 super window channel are stable relative to the RTG.SST at the better than 8 mK/year level, and the spectral calibration is stable at the 1 ppm/year level. The excellent stability and accuracy are the result of the implementation of AIRS as a grating array spectrometer, which is cooled and stabilized within 10 mK at 155 K. Analysis of daily measurements of the temperature gradient between the surface and 7 km altitude show that the AIRS Calibration Data Subset has applications which extend its original intent for calibration support to climate research. The Atmospheric Infrared Sounder (AIRS) on the EOS Aqua satellite was launched into polar orbit in May 2002. AIRS covers the spectral region from 640 to 2700 cm-1 with 2378 independent channels and represents the first of a new generation of hyper spectral resolution sounders in support of global sounding data for weather forecasting and climate research.

infrared↗