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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

GC13I-0857: Designing a Frost Forecasting Service for Small Scale Tea Farmers in East Africa

Kenya is the third largest tea exporter in the world, producing 10% of the world's black tea. Sixty percent of this production occurs largely by small scale tea holders, with an average farm size of 1.04 acres, and an annual net income of $1,075. According to a recent evaluation, a typical frost event in the tea growing region causes about $200 dollars in losses which can be catastrophic for a small holder farm. A 72-hour frost forecast would provide these small-scale tea farmers with enough notice to reduce losses by approximately 80 USD annually. With this knowledge, SERVIR, a joint NASA-USAID initiative that brings Earth observations for improved decision making in developing countries, sought to design a frost monitoring and forecasting service that would provide farmers with enough lead time to react to and protect against a forecasted frost occurrence on their farm. SERVIR Eastern and Southern Africa, through its implementing partner, the Regional Centre for Mapping of Resources for Development (RCMRD), designed a service that included multiple stakeholder engagement events whereby stakeholders from the tea industry value chain were invited to share their experiences so that the exact needs and flow of information could be identified. This unique event allowed enabled the design of a service that fit the specifications of the stakeholders. The monitoring service component uses the MODIS Land Surface Temperature product to identify frost occurrences in near-real time. The prediction component, currently under testing, uses the 2-m air temperature, relative humidity, and 10-m wind speed from a series of high-resolution Weather Research and Forecasting (WRF) numerical weather prediction model runs over eastern Kenya as inputs into a frost prediction algorithm. Accuracy and sensitivity of the algorithm is being assessed with observations collected from the farmers using a smart phone app developed specifically to report frost occurrences, and from data shared through our partner network developed at the stakeholder engagement meeting. This presentation will illustrate the efficacy of our frost forecasting algorithm, and a way forward for incorporating these forecasts in a meaningful way to the key decision makers - the small-scale farmers of East Africa.

frost↗
DOE OSTI2022

Predictability and empirical dynamics of fisheries time series in the North Pacific

Previous studies have documented a strong relationship between marine ecosystems and large-scale modes of sea surface height (SSH) and sea surface temperature (SST) variability in the North Pacific such as the Pacific Decadal Oscillation and the North Pacific Gyre Oscillation. In the central and western North Pacific along the Kuroshio-Oyashio Extension (KOE), the expression of these modes in SSH and SST is linked to the propagation of long oceanic Rossby waves, which extend the predictability of the climate system to ~3 years. Using a multivariate physical-biological linear inverse model (LIM) we explore the extent to which this physical predictability leads to multi-year prediction of dominant fishery indicators inferred from three datasets (i.e., estimated biomasses, landings, and catches). We find that despite the strong autocorrelation in the fish indicators, the LIM adds dynamical forecast skill beyond persistence up to 5-6 years. By performing a sensitivity analysis of the LIM forecast model, we find that two main factors are essential for extending the dynamical predictability of the fishery indicators beyond persistence. The first is the interaction of the fishery indicators with the SST/SSH of the North and tropical Pacific. The second is the empirical relationship among the fisheries time series. This latter component reflects stock-stock interactions as well as common technological and human socioeconomic factors that may influence multiple fisheries and are captured in the training of the LIM. These results suggest that empirical dynamical models and machine learning algorithms, such as the LIM, provide an alternative and promising approach for forecasting key ecological indicators beyond the skill of persistence.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1976

Classification and machine recognition of severe weather patterns

Forecasting and warning of severe weather conditions are treated from the vantage point of pattern recognition by machine. Pictorial patterns and waveform patterns are distinguished. Time series data on sferics are dealt with by considering waveform patterns. A severe storm patterns recognition machine is described, along with schemes for detection via cross-correlation of time series (same channel or different channels). Syntactic and decision-theoretic approaches to feature extraction are discussed. Active and decayed tornados and thunderstorms, lightning discharges, and funnels and their related time series data are studied.

Wang, P. P.↗
NASA NTRS2015

A Linearized Prognostic Cloud Scheme in NASAs Goddard Earth Observing System Data Assimilation Tools

A linearized prognostic cloud scheme has been developed to accompany the linearized convection scheme recently implemented in NASA's Goddard Earth Observing System data assimilation tools. The linearization, developed from the nonlinear cloud scheme, treats cloud variables prognostically so they are subject to linearized advection, diffusion, generation, and evaporation. Four linearized cloud variables are modeled, the ice and water phases of clouds generated by large-scale condensation and, separately, by detraining convection. For each species the scheme models their sources, sublimation, evaporation, and autoconversion. Large-scale, anvil and convective species of precipitation are modeled and evaporated. The cloud scheme exhibits linearity and realistic perturbation growth, except around the generation of clouds through large-scale condensation. Discontinuities and steep gradients are widely used here and severe problems occur in the calculation of cloud fraction. For data assimilation applications this poor behavior is controlled by replacing this part of the scheme with a perturbation model. For observation impacts, where efficiency is less of a concern, a filtering is developed that examines the Jacobian. The replacement scheme is only invoked if Jacobian elements or eigenvalues violate a series of tuned constants. The linearized prognostic cloud scheme is tested by comparing the linear and nonlinear perturbation trajectories for 6-, 12-, and 24-h forecast times. The tangent linear model performs well and perturbations of clouds are well captured for the lead times of interest.

Precipitation↗
DOE OSTI2021

Numerical Simulations of Geologic Storage Reservoir Management to Support Risk Mitigation Evaluation

This report provides a detailed description of a set of numerical simulations that represent reservoir behavior over time in response to different operational decision scenarios for detection of potential leakage and reduction or avoidance of leakage impact at a hypothetical geological carbon storage (GCS) site. These simulations serve as the basis for a series of GCS leakage risk forecasts that are to be developed using the National Risk Assessment Partnership’s Open-Source Integrated Assessment Model (NRAP-Open-IAM), and a demonstration of a simple decision support workflow for evaluation of mitigation strategies based on results of those system model forecasts. This risk assessment and decision support study is forthcoming. Four injection scenarios were considered: a constant rate carbon dioxide (CO 2 ) injection case (base case), a case with CO 2 injection rate adjustment, a case with early termination of injection operations, and a case with brine extraction. CO 2 injection operations were controlled to ensure that the pressure transient remains below the defined manageable reservoir fracture pressure, with consideration shown to hypothetical locations within the modeled spatial domain where the overburden was weaker and lower transient pressure increases were allowable. Additionally, a brine extraction alternative was considered as a reservoir management and risk mitigation option to reduce reservoir pressure, steer the plume away from any hypothetical geohazard such as fault as needed, and enhance storage capacity. Such operational actions contribute to risk management overtime. This study explores the potential utility of reservoir management for risk reduction at GCS sites. This study shows that the injection design may modify the time to CO 2 breakthrough at a legacy well; in particular, these results show that brine extraction can add value for mitigating risk both by delaying leakage and reducing pressure build-up. For the scenario considered, both pressure plots and pressure distributions demonstrate that pressure build-up was decreased by 3% with brine extraction. Additionally, extraction of brine afforded enhancement of CO 2 storage capacity by 5% compared to the base case. These findings suggest that brine extraction has substantial potential to steer the risk-related reservoir effects away from known geohazards (e.g., faults and legacy wells) by conducting pressure transient effects and CO 2 plume movement toward the production well. Injection rate adjustment scenarios considered in this study show potential value for managing both reservoir pressure transients and CO 2 plume behavior. Operational actions for reducing injection and/or early termination of injection (as compared to the base case), however, require careful design; tailoring both the extent and timing of injection rate adjustment over the injection and post-injection operational period must be thoroughly planned to balance maximizing storage and minimizing subsurface environmental risk. The study also gives preliminary consideration to the effectiveness that monitoring strategy may play in providing useful information to inform reservoir management decisions for risk reduction. Two types of monitoring were considered: 1) pressure build-up or pressure transient; and 2) potential leakage detection from a CO 2 mass or plume. Four hypothetical legacy wells, two plugged and two abandoned, were placed in the model domain. Monitoring along these wells was measured over time in individual stacked reservoir formations and shale formations to support risk mitigation decisions, especially operational decisions that assisted in risk reduction. These simulations will serve as the basis for a series of GCS leakage risk forecasts that are to be developed using NRAP’s Open-IAM, and demonstration of a simple decision support workflow for comparative assessment of mitigation alternatives based on results of those system model forecasts. This risk assessment and decision support study is forthcoming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Application of a Genetic Algorithm to the Optimization of a Mesoscale Model for Emergency Response

Besides solving the equations of momentum, heat, and moisture transport on the model grid, mesoscale weather models must account for subgrid-scale processes that affect the resolved model variables. These are simulated with model parameterizations, which often rely on values preset by the user. Such “free” model parameters, along with others set to initialize the model, are often poorly constrained, requiring that a user select each from a range of plausible values. Finding the values to optimize any forecasting tool can be accomplished with a search algorithm, and one such process—the genetic algorithm (GA)—has become especially popular. As applied to modeling, GAs represent a Darwinian process: an ensemble of simulations is run with a different set of parameter values for each member, and the members subsequently judged to be most accurate are selected as “parents” who pass their parameters onto a new generation. At the U.S. Department of Energy’s Savannah River Site in South Carolina, we are applying a GA to the Regional Atmospheric Modeling System (RAMS) mesoscale weather model, which supplies input to a model to simulate the dispersion of an airborne contaminant as part of the site’s emergency response preparations. An ensemble of forecasts is run each day, weather data are used to “score” the individual members of the ensemble, and the parameters from the best members are used for the next day’s forecasts. As meteorological conditions change, the parameters change as well, maintaining a model configuration that is best adapted to atmospheric conditions. Significance Statement We wanted to develop a forecasting system by which a weather model is run over the Savannah River Site each day and repeatedly adjusted according to how well it performed the previous day. To run the model, a series of values (parameters) must be set to control how the model will calculate winds, temperatures, and other desired variables. Each day the model was run several times using different combinations of these parameters and later compared with observed meteorological conditions. Parameters that produced the most accurate forecasts were preferentially reused to create the forecasts for the next day. The process was tested for the summer of 2020 and exhibited lower errors than forecasts produced by the model using default values of the parameters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

PVInsight (Final Technical Report)

Data generated from real-world photovoltaic (PV) systems represent significant opportunities for the industry–from digital operations and maintenance to real-time planning and forecasting. However, these data also come with substantial, unique challenges. A particular challenge is the analysis of unlabeled PV performance data, which we define as time-series measurements of real power production (or sometimes current or voltage) that do not have corresponding meteorological measurements (irradiance, temperature, etc.) or system configuration information (sometimes called system metadata). These challenges are amplified in the distributed rooftop sector, in which data quality, completeness, and metadata can be very poor.

14 SOLAR ENERGY↗
NASA NTRS2021

TLife-LSTM: Forecasting Future COVID-19 Progression with Topological Signatures of Atmospheric Conditions

Understanding the impact of atmospheric conditions on SARS-CoV2 is critical to model COVID-19 dynamics and sheds a light on the future spread around the world. Furthermore, geographic distri- butions of expected clinical severity of COVID-19 may be closely linked to prior history of respiratory diseases and changes in humidity, tem- perature, and air quality. In this context, we postulate that by tracking topological features of atmospheric conditions over time, we can provide a quanti?able structural distribution of atmospheric changes that are likely to be related to COVID-19 dynamics. As such, we apply the machinery of persistence homology on time series of graphs to extract topological signatures and to follow geographical changes in relative humidity and temperature. We develop an integrative machine learning framework named Topological Lifespan LSTM (TLife-LSTM) and test its predictive capabilities on forecasting the dynamics of SARS-CoV2 cases. We validate our framework using the number of con?rmed cases and hospitalization rates recorded in the states of Washington and California in the USA. Our results demonstrate the predictive potential of TLife-LSTM in forecasting the dynamics of COVID-19 and modeling its complex spatio-temporal spread dynamics.

Gel, Yulia R.↗
NASA NTRS2014

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Error Covariance↗
NASA NTRS2014

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Data Assimilation↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗
DOE OSTI2025

Temporal Forecasting of Distributed Temperature Sensing in a Thermal Hydraulic System With Machine Learning and Statistical Models

We benchmark performance of long-short term memory (LSTM) network machine learning model and autoregressive integrated moving average (ARIMA) statistical model in temporal forecasting of distributed temperature sensing (DTS). Data in this study consists of fluid temperature transient measured with two co-located Rayleigh scattering fiber optic sensors (FOS) in a forced convection mixing zone of a thermal tee. We treat each gauge of a FOS as an independent temperature sensor. We first study prediction of DTS time series using Vanilla LSTM and ARIMA models trained on prior history of the same FOS that is used for testing. The results yield maximum absolute percentage error (MaxAPE) and root mean squared percentage error (RMSPE) of 1.58% and 0.06% for ARIMA, and 3.14% and 0.44% for LSTM, respectively. Next, we investigate zero-shot forecasting (ZSF) with LSTM and ARIMA trained on history of the co-located FOS only, which is advantageous when limited training data is available. The ZSF MaxAPE and RMSPE values for ARIMA are comparable to those of the Vanilla use case, while the error values for LSTM increase. We show that in ZSF, performance of LSTM network can be improved by training on most correlated gauges between the two FOS, which are identified by calculating the Pearson correlation coefficient. The improved ZSF MaxAPE and RMSPE for LSTM are 4.4% and 0.33%, respectively. Performance of ZSF LSTM can be further enhanced through transfer learning (TL), where LSTM is re-trained on a subset of the FOS that is the target of forecasting. We show that LSTM pre-trained on correlated dataset and re-trained on 30% of testing target dataset achieves MaxAPE and RMSPE values of 2.32% and 0.28%, respectively.

ARIMA↗
NASA NTRS2003

Use of EOS Data in AWIPS for Weather Forecasting

Operational weather forecasting relies heavily on real time data and modeling products for forecast preparation and dissemination of significant weather information to the public. The synthesis of this information (observations and model products) by the meteorologist is facilitated by a decision support system to display and integrate the information in a useful fashion. For the NWS this system is called Advanced Weather Interactive Processing System (AWIPS). Over the last few years NASA has launched a series of new Earth Observation Satellites (EOS) for climate monitoring that include several instruments that provide high-resolution measurements of atmospheric and surface features important for weather forecasting and analysis. The key to the utilization of these unique new measurements by the NWS is the real time integration of the EOS data into the AWIPS system. This is currently being done in the Huntsville and Birmingham NWS Forecast Offices under the NASA Short-term Prediction Research and Transition (SPORT) Program. This paper describes the use of near real time MODIS and AIRS data in AWIPS to improve the detection of clouds, moisture variations, atmospheric stability, and thermal signatures that can lead to significant weather development. The paper and the conference presentation will focus on several examples where MODIS and AIRS data have made a positive impact on forecast accuracy. The results of an assessment of the utility of these products for weather forecast improvement made at the Huntsville NWS Forecast Office will be presented.

Jedlovec, Gary J.↗
DOE OSTI2022

PVAnalytics: A Python Package for Automated Processing of Solar Time Series Data

Multiple publicly available software packages exist that analyze solar time series data, including RdTools and Solar Data Tools, among others. Several of these packages contain their own unique quality assurance (QA) and feature recognition algorithms. The python PVAnalytics package was developed to offer an internally consistent source for these analysis tools, making it easier for the end user to deploy these routines on his or her solar data. The PVAnalytics package currently contains routines for outlier detection, inverter clipping detection, irradiance and temperature checks, orientation checks, and data shift detection, among other functions. These functions have been aggregated from various sources including Solar Forecast Arbiter, RdTools, and the QA process developed by NREL's PV Fleets Initiative. We are continuously adding new functionality to the package, including documentation, examples and algorithms. By bundling QA functionality into a single software package, we hope to make PVAnalytics a comprehensive software library to support analysis of solar metadata and time series data.

data cleaning↗
NASA NTRS2010

The Goes-R Geostationary Lightning Mapper (GLM): Algorithm and Instrument Status

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved capability for the Advanced Baseline Imager (ABI). The Geostationary Lighting Mapper (GLM) will map total lightning activity (in-cloud and cloud-to-ground lighting flashes) continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (a prototype and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms, cal/val performance monitoring tools, and new applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. A joint field campaign with Brazilian researchers in 2010-2011 will produce concurrent observations from a VHF lightning mapping array, Meteosat multi-band imagery, Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) overpasses, and related ground and in-situ lightning and meteorological measurements in the vicinity of Sao Paulo. These data will provide a new comprehensive proxy data set for algorithm and application development.

Goodman, Steven J.↗
NASA NTRS2011

The GOES-R Series Geostationary Lightning Mapper (GLM)

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), which will have just completed Critical Design Review and move forward into the construction phase of instrument development. The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (an engineering development unit and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms, cal/val performance monitoring tools, and new applications. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms

Goodman, Steven J.↗
NASA NTRS2011

The GOES-R GeoStationary Lightning Mapper (GLM)

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved capability for the Advanced Baseline Imager (ABI). The Geostationary Lighting Mapper (GLM) will map total lightning activity (in-cloud and cloud-to-ground lighting flashes) continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency among a number of potential applications. In parallel with the instrument development (a prototype and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms (environmental data records), cal/val performance monitoring tools, and new applications using GLM alone, in combination with the ABI, merged with ground-based sensors, and decision aids augmented by numerical weather prediction model forecasts. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. An international field campaign planned for 2011-2012 will produce concurrent observations from a VHF lightning mapping array, Meteosat multi-band imagery, Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) overpasses, and related ground and in-situ lightning and meteorological measurements in the vicinity of Sao Paulo. These data will provide a new comprehensive proxy data set for algorithm and application development.

Goodman, Steven J.↗
NASA NTRS2011

The Goes-R Geostationary Lightning Mapper (GLM)

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved storm diagnostic capability with the Advanced Baseline Imager. The GLM will map total lightning activity (in-cloud and cloud-to-ground lighting flashes) continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms, cal/val performance monitoring tools, and new applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. In this paper we will report on new Nowcasting and storm warning applications being developed and evaluated at various NOAA Testbeds.

Goodman, Steven J.↗