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NASA NTRS2009

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↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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

Lauren C. Andrews↗

NASA NTRS2011

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.↗

NASA NTRSDate not supplied

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↗

NASA NTRS2012

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↗

NASA NTRS2019

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↗

NASA NTRS2004

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↗

NASA NTRS2018

Microphysics and Radiation Effect of Dust on Saharan Air Layer: An HS3 Case Study

A Saharan air layer (SAL) event associated with a nondeveloping African easterly wave (AEW) over the main development region of the eastern Atlantic was sampled by the NASA Global Hawk aircraft on 24-25 August 2013 during the NASA Hurricane and Severe Storm Sentinel (HS3) campaign and was simulated with the NASA Unified Weather Research and Forecasting (NU-WRF) Model. Airborne, ground-based, and spaceborne measurements were used to evaluate the model performance. The microphysical and radiative effects of dust and other aerosols on the SAL structure and environment were investigated with the factor-separation method. The results indicate that relative to a simulation without dust-radiative and microphysical impacts, Saharan dust and other aerosols heated the SAL air mainly through shortwave heating by the direct aerosol-radiation (AR) effect, resulting in a warmer (up to 0.6 K) and drier (up to 5% RH reduction) SAL and maintaining the strong temperature inversion at the base of the SAL in the presence of predominant longwave cooling. Radiative heating of the dust accentuated a vertical circulation within the dust layer, in which air rose (sank) in the northern (southern) portions of the dust layer. Furthermore, above and to the south of the dust layer, both the microphysical and radiative impacts of dust tended to counter the vertical motions associated with the Hadley circulation, causing a small weakening and southward shift of convection in the intertropical convergence zone (ITCZ) and reduced anvil cloud to the north. Changes in moisture and cloud/precipitation hydrometeors were largely driven by the dust induced changes in vertical motion. Dust strengthened the African easterly jet by up to ~1ms(exp -1) at the southern edge of the jet, primarily through the AR effect, and produced modest increases in vertical wind shear within and in the vicinity of the dust layer. These modulations of the SAL and AEW environment clearly contributed to the nondevelopment of this AEW.

Tao, Zhining↗

NASA NTRSDate not supplied

Enabling Interoperability in Earth System Digital Twins (ESDT): Integrating Observations, Models, and AI for Actionable Insights Through NASA'S Intelligent Systems Technology Program

NASA’s Intelligent Systems Technology Program (IST) is driving a paradigm shift in Earth science through the development of Earth System Digital Twins (ESDT). These integrated information systems create a dynamic "digital replica" of the Earth by harmonizing continuous, multi-source observations with high-fidelity models and state-of-the-art artificial intelligence (AI) that enable “What now?”, “What next?”, and “What if?” scenario building. These scenarios are reflected in NASA IST’s series of ESDTs, from the Coastal Zone Digital Twin that integrates complex data on the current state of the Chesapeake Bay to the Terrestrial Environmental Rapid-Replication and Assimilation Hydrometeorological (TerraHydro) AI-based ESDT that forecasts water movement across Earth’s surface, to the Agriculture Land Information System (AgLIS) which can be used to assess optimal planting dates and crop yield estimates. By bridging the gap between vast data archives and actionable insights, these projects enable a system-of-systems approach to understanding complex, interacting Earth processes. This poster will highlight recent innovations and future directions from NASA’s ESDT initiatives: Continuous Data Assimilation & Multi-Source Fusion. A core requirement of the ESDT work is the transition from static models to dynamic "living" replicas. This involves creating frameworks for the continual assimilation of near-real-time data from uncoordinated, heterogeneous sources, including satellite observations and airborne assets, and ground-based Internet of Things (IoT) sensors. These systems link design, operational status, and environmental data, ensuring the digital twin accurately reflects the current state of the physical Earth system. High-Fidelity Hybrid Modeling & Computational Acceleration to enable interactive "what-if" explorations, programs are moving beyond traditional, slow physical solvers by developing fast surrogate machine learning models and Deep Generative Models (DGMs). These hybrid approaches use neural networks to emulate complex physics, such as cloud feedback or ocean dynamics, at a fraction of the original computing cost, often leveraging advanced hardware like Graphics Processing Units (GPUs) to achieve the necessary scale. Federated Ecosystems & Interoperable Frameworks rather than building isolated tools, NASA IST is moving toward federated ESDTs and reusable analytic collaborative frameworks. This theme focuses on interoperability standards and common ontologies that allow specialized digital twins to interact and share data. This system-of-systems architecture supports multi-discipline investigations, such as analyzing how upstream watershed changes impact downstream urban flooding or how wildfire emissions affect regional air quality. By leveraging these advancements, ESDTs empower researchers and decision-makers to conduct real-time analysis and run complex hypothetical scenarios, ultimately improving our understanding of Earth’s evolving systems and informing critical real-world applications.

Earth System↗

NASA NTRS2014

Comparison of Climatological Planetary Boundary Layer Depth Estimates Using the GEOS-5 AGCM

Planetary boundary layer (PBL) processes, including those influencing the PBL depth, control many aspects of weather and climate and accurate models of these processes are important for forecasting changes in the future. However, evaluation of model estimates of PBL depth are difficult because no consensus on PBL depth definition currently exists and various methods for estimating this parameter can give results that differ by hundreds of meters or more. In order to facilitate comparisons between the Goddard Earth Observation System (GEOS-5) and other modeling and observational systems, seven PBL depth estimation methods are used to produce PBL depth climatologies and are evaluated and compared here. All seven methods evaluate the same atmosphere so all differences are related solely to the definition chosen. These methods depend on the scalar diffusivity, bulk and local Richardson numbers, and the diagnosed horizontal turbulent kinetic energy (TKE). Results are aggregated by climate class in order to allow broad generalizations. The various PBL depth estimations give similar midday results with some exceptions. One method based on horizontal turbulent kinetic energy produces deeper PBL depths in the winter associated with winter storms. In warm, moist conditions, the method based on a bulk Richardson number gives results that are shallower than those given by the methods based on the scalar diffusivity. The impact of turbulence driven by radiative cooling at cloud top is most significant during the evening transition and along several regions across the oceans and methods sensitive to this cooling produce deeper PBL depths where it is most active. Additionally, Richardson number-based methods collapse better at night than methods that depend on the scalar diffusivity. This feature potentially affects tracer transport.

Planetary Boundary Layer Depth↗

NASA NTRS2019

Accelerated Simulation of Air Pollution Using NVIDIA RAPIDS

Atmospheric chemistry models are a central tool to study and forecast the impact of air pollution on the environment, vegetation, and human health. However, the numerical simulation of chemical kinetics is computationally expensive due to the stiffness of the system of ordinary differential equations that describes atmospheric chemistry. Here we present an alternative approach to the computation of atmospheric chemistry based on machine learning. Our training data set is produced using the NASA Goddard Earth Observing System (GEOS) model with GEOS-Chem chemistry, run on the NASA Center for Climate Simulation (NCCS) Discover supercomputing cluster on 384 Intel Xeon Haswell cores. This model spends more than 50% of total run time on solving atmospheric chemistry. The data set contains as input features the air pollution concentrations before solving the differential equations, together with some key physical parameters such as temperature and sun intensity. As target variables we define the air pollution concentrations after solving the differential equations. Using Dask-cuDF and Dask-XGBoost on the NVIDIA RAPIDS platform on 8 Tesla V100 GPUs, we generate from this training set gradient boosted decision tree models that can reproduce the simulation of chemical kinetics. We do this on the NCCS Advanced Data Analytics Platform (ADAPT) science cloud environment. Our application takes full advantage of recent advances in Dask-XGBoost, such as multi-node and multi-GPU scaling for distributed training with large data sets. The increase in training data size enabled by this is critical to capture the full range of chemical environments encountered across the globe and all annual seasons.The boosted tree models offer good predictability and show many of the features of the full chemistry reference simulation. Further improvements can be achieved through mass balance considerations and by accounting for error correlations. We incorporate the boosted tree models into the GEOS reference model using XGBoost's C API. This enables a seamless integration of the GPU trained models into GEOS-Chem, which is written in Fortran and optimized for use in a massively parallel CPU environment. We show the benefits of this approach and discuss the potential speedup of this machine learning accelerated atmospheric chemistry model.

Keller, Christoph A.↗

NASA NTRS2014

A Comparison of the Red Green Blue Air Mass Imagery and Hyperspectral Infrared Retrieved Profiles

The Red Green Blue (RGB) Air Mass imagery is derived from multiple channels or paired channel differences. Multiple channel products typically provide additional information than a single channel can provide alone. The RGB Air Mass imagery simplifies the interpretation of temperature and moisture characteristics of air masses surrounding synoptic and mesoscale features. Despite the ease of interpretation of multiple channel products, the combination of channels and channel differences means the resulting product does not represent a quantity or physical parameter such as brightness temperature in conventional single channel satellite imagery. Without a specific quantity to reference, forecasters are often confused as to what RGB products represent. Hyperspectral infrared retrieved profiles of temperature, moisture, and ozone can provide insight about the air mass represented on the RGB Air Mass product and provide confidence in the product and representation of air masses despite the lack of a quantity to reference for interpretation. This study focuses on RGB Air Mass analysis of Hurricane Sandy as it moved north along the U.S. East Coast, while transitioning to a hybrid extratropical storm. Soundings and total column ozone retrievals were analyzed using data from the Cross-track Infrared and Advanced Technology Microwave Sounder Suite (CrIMSS) on the Suomi National Polar Orbiting Partnership satellite and the Atmospheric Infrared Sounder (AIRS) on the National Aeronautics and Space Administration Aqua satellite along with dropsondes that were collected from National Oceanic and Atmospheric Administration and Air Force research aircraft. By comparing these datasets to the RGB Air Mass, it is possible to capture quantitative information that could help in analyzing the synoptic environment enough to diagnose the onset of extratropical transition. This was done by identifying any stratospheric air intrusions (SAIs) that existed in the vicinity of Sandy as the wind field expanded and the cloud pattern evolved into an atypical pattern.

Berndt, E. B.↗

NASA NTRS2018

An Initial Validation of Landsat 5 and 7 Derived Surface Water Temperature for U.S. Lakes, Reservoirs, and Estuaries

The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34 C in lake pixels (is) greater than180 m from land, 4.89 C at the land-water boundary, and 1.11 C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.

Schaeffer, Blake A.↗

NASA NTRS2019

Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm

Precipitation modifies 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 for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol–cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION).

Simone Lolli↗

NASA NTRS2000

Hurricane Wind Field Measurements with Scanning Airborne Doppler Lidar During CAMEX-3

During the 1998 Convection and Moisture Experiment (CAMEX-3), the first hurricane wind field measurements with Doppler lidar were achieved. Wind fields were mapped within the eye, along the eyewall, in the central dense overcast, and in the marine boundary layer encompassing the inflow region. Spatial coverage was determined primarily by cloud distribution and opacity. Within optically-thin cirrus slant range of 20- 25 km was achieved, whereas no propagation was obtained during penetration of dense cloud. Measurements were obtained with the Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) on the NASA DC-8 research aircraft. MACAWS was developed and operated cooperatively by the atmospheric lidar remote sensing groups of NOAA Environmental Technology Laboratory, NASA Marshall Space Flight Center, and Jet Propulsion Laboratory. A pseudo-dual Doppler technique ("co-planar scanning") is used to map the horizontal component of the wind at several vertical levels. Pulses from the laser are directed out the left side of the aircraft in the desired directions using computer-controlled rotating prisms. Upon exiting the aircraft, the beam is completely eyesafe. Aircraft attitude and speed are taken into account during real-time signal processing, resulting in determination of the ground-relative wind to an accuracy of about 1 m/s magnitude and about 10 deg direction. Beam pointing angle errors are about 0.1 deg, equivalent to about 17 m at 10 km. Horizontal resolution is about 1 km (along-track) for typical signal processor and scanner settings; vertical resolution varies with range. Results from CAMEX-3 suggest that scanning Doppler wind lidar can complement airborne Doppler radar by providing wind field measurements in regions that are devoid of hydrometeors. At present MACAWS observations are being assimilated into experimental forecast models and satellite Doppler wind lidar simulations to evaluate the relative impact.

Rothermel, Jeffry↗

NASA NTRS1994

Lightning studies using LDAR and companion data sets

Research was conducted to use the KSC Lightning Detection and Ranging (LDAR) system, together with companion data, in four subprojects: weather forecasting and advisory applications of LDAR, LDAR in relation to field mill readings, lightning flash and stroke detection using LDAR, and LDAR in relation to radar reflectivity patterns and KSC wind profiler vertical velocities. The research is aimed at developing rules, algorithms, and training materials that can be used by the operational weather forecasters who issue weather advisories for daily ground operations and launches by NASA and the United States Air Force. During the summer of 1993, LDAR data was examined on an hourly basis from 14 thunderstorm days and compared to ground strike data measured by the Lightning Location and Protection (LLP) system. These data were re-examined during 1994 to identify, number, and track LDAR-detected storms continually throughout the day and avoid certain interpretation problems arising from the use of hourly files. An areal storm growth factor was incorporated into a scheme to use current mappings of LDAR-defined thunderstorms to predict future ground strikes. During the summer of 1994, extensive sets of LDAR and companion data have been collected for 16 thunderstorm days, including a variety of meteorological situations. Detailed case studies are being conducted to relate the occurence of LDAR to the radar structure and evolution of thunderstorms. Field mill (LPWS) data are being examined to evaluate the complementary nature of LDAR and LPLWS data in determining the time of beginning and ending of the ground strike threat at critical sites. A computerized lightning flash and stroke discrimination algorithm has been written that can be used to help locate the points of origin of the electrical discharges, help distinguish in-cloud, cloud-ground, and upward flashes, and perhaps determine when the threat of ground strikes has ceased. Surface wind tower (mesonet), radar, sounding, and KSC wind profiler data will be used to develop schemes to help anticipate the timing and location of new thunderstorm development. Analysis of this data will continue in graduate student research projects.

Forbes, Gregory S.↗