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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

DC and Structured Electric Fields Observed on the C/NOFS Satellite and Their Association with Longitude, Plasma Density, and Solar Activity

Observations of DC electric fields and associated E x B plasma drifts gathered by the Vector Electric Field Investigation (VEFI) on the Air Force Communication/Navigation Outage Forecasting System (C/NOFS) satellite are presented. We show statistical averages of the vector fields and resulting E x B plasma flows for the first three years of operations as a function of season, longitude, local time, and Fl 0.7 conditions. Magnetic field data from the VEFI science magnetometer are used to compute the plasma flows. Although typically displaying eastward and outward-directed fields during the day and westward and downward-directed fields at night, the data from DC electric field detector often reveal variations from this pattern that depend on longitude, solar activity, and plasma density. Clear "wave-4" tidal effects in both electric field components have been detected and will be presented. Zonal plasma drifts show a marked variation with solar activity and may be used as a proxy for neutral winds at night. Evidence for pre-reversal enhancements in the meridional drifts that depend on solar activity is present for some longitudes, and are corroborated by clear evidence in the plasma density data that the spacecraft journeyed below the F-peak during evenings when the rise in the ionosphere is most pronounced. In addition to DC electric fields, the data reveal considerable electric field structures at large scales (approx 100's of km) that are usually confined to the nightside. Although such electric field structures are typically associated with plasma density depletions and structures, what is surprising is the number of cases in which large amplitude, structured DC electric fields are observed without a significant plasma density counterpart structure, including their appearance at times when the ambient plasma density appears relatively quiescent. We investigate the mapping of structured electric fields along magnetic field lines from distant locations and consider tropospheric thunderstorm sources to explain some of the observations. The dependence of the structuring on season and solar activity will be reported and discussed.

Pfaff, Robert↗
NASA NTRS2004

Simulation of Wind Profile Perturbations for Launch Vehicle Design

Ideally, a statistically representative sample of measured high-resolution wind profiles with wavelengths as small as tens of meters is required in design studies to establish aerodynamic load indicator dispersions and vehicle control system capability. At most potential launch sites, high- resolution wind profiles may not exist. Representative samples of Rawinsonde wind profiles to altitudes of 30 km are more likely to be available from the extensive network of measurement sites established for routine sampling in support of weather observing and forecasting activity. Such a sample, large enough to be statistically representative of relatively large wavelength perturbations, would be inadequate for launch vehicle design assessments because the Rawinsonde system accurately measures wind perturbations with wavelengths no smaller than 2000 m (1000 m altitude increment). The Kennedy Space Center (KSC) Jimsphere wind profiles (150/month and seasonal 2 and 3.5-hr pairs) are the only adequate samples of high resolution profiles approx. 150 to 300 m effective resolution, but over-sampled at 25 m intervals) that have been used extensively for launch vehicle design assessments. Therefore, a simulation process has been developed for enhancement of measured low-resolution Rawinsonde profiles that would be applicable in preliminary launch vehicle design studies at launch sites other than KSC.

Adelfang, S. I.↗
NASA NTRS2011

MesoNAM Verification Phase II

The 45th Weather Squadron Launch Weather Officers use the 12-km resolution North American Mesoscale model (MesoNAM) forecasts to support launch weather operations. In Phase I, the performance of the model at KSC/CCAFS was measured objectively by conducting a detailed statistical analysis of model output compared to observed values. The objective analysis compared the MesoNAM forecast winds, temperature, and dew point to the observed values from the sensors in the KSC/CCAFS wind tower network. In Phase II, the AMU modified the current tool by adding an additional 15 months of model output to the database and recalculating the verification statistics. The bias, standard deviation of bias, Root Mean Square Error, and Hypothesis test for bias were calculated to verify the performance of the model. The results indicated that the accuracy decreased as the forecast progressed, there was a diurnal signal in temperature with a cool bias during the late night and a warm bias during the afternoon, and there was a diurnal signal in dewpoint temperature with a low bias during the afternoon and a high bias during the late night.

Watson, Leela R.↗
DOE OSTI2020

Probabilistic Solar Power Forecasting Using Bayesian Model Averaging

There is rising interest in probabilistic forecasting to mitigate risks from solar power uncertainty, but the numerical weather prediction (NWP) ensembles readily available to system operators are often biased and underdispersed. We propose a Bayesian model averaging (BMA) post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead timescale. BMA is a kernel dressing technique for NWP ensembles in which the forecast is a weighted sum of member-specific probability density functions. We tailor BMA for utility-scale PV forecasting by modeling power clipping at the AC inverter rating and advance the theory of BMA with a new beta kernel parameterization that accommodates theoretical constraints not previously addressed. BMA is demonstrated for a case study of 11 utility-scale PV plants in Texas, forecasting at hourly resolution for the complete year 2018. BMA's mixture-model approach mitigates underdispersion of the raw ensemble to significantly improve forecast calibration, while consistently outperforming an ensemble model output statistics (EMOS) parametric approach from the literature. At 4-hour lead time, the BMA post-processing achieves continuous ranked probability skill scores of 2--36% over the raw ensemble, with consistent performance at multiple lead times suitable for power system operations.

14 SOLAR ENERGY↗
NASA NTRS2003

Simulation of Wind Profile Perturbations for Launch Vehicle Ascent Flight Systems Design Assessments

Ideally, a statistically representative sample of measured high-resolution wind profiles with wavelengths as small as tens of meters is required for assessment of launch vehicle ascent flight systems component capability and vehicle operability for a selected launch site. At most potential launch sites a sample of high-resolution measured wind profiles may not exist. Representative samples of Rawinsonde wind profiles are more likely to be available because of the extensive network of measurement sites established for routine measurements at 12-hr intervals in support of national and international weather observing and forecasting activity. Such a sample, although large enough to statistically represent relatively large wavelength perturbations, would be inadequate for launch system design assessment applications because the Rawinsonde system can accurately measure wind perturbations with wavelengths no smaller than 2000m (1000m altitude increment). Wavelengths less than 2000m in the raw Rawinsonde data, which tend to be dominated by un-damped spurious balloon motion and radar tracking system noise, are filtered within the data processing scheme. The Kennedy Space Center (KSC) Jimsphere wind profiles (150/month and seasonal pairs) are the only adequate high resolution (approximately 150 to 300m effective resolution, but over-sampled at 25m intervals) data that have been used extensively in launch vehicle design, operability assessments and operational protection of vehicle systems for wind perturbation uncertainty. Jimsphere wind profiles have been measured at a few other potential launch sites but the number of profiles is relatively small and the samples are not statistically representative of the site dependent wind profile variability. A simulation process has been developed for enhancement of measured low-resolution Rawinsonde profiles that are more likely to be available in the vicinity of potential launch sites and are a statistically representative sample of wind profile perturbation wavelengths greater than 2000m. The enhancement produces perturbed wind profiles with wavelengths as small as desired for application in launch vehicle ascent flight simulations and design assessments.

Adelfang, S. I.↗
DOE OSTI2020

Updraft and Downdraft Core Size and Intensity as Revealed by Radar Wind Profilers: MCS Observations and Idealized Model Comparisons

This study explores the updraft and downdraft properties of mature stage mesoscale convective systems (MCSs) in terms of draft core width, shape, intensity, and mass flux characteristics. The observations use extended radar wind profiler (RWP) and surveillance radar datasets from the U.S. Department of Energy Atmospheric Radiation Measurement program for midlatitude (Oklahoma, USA) and tropical (Amazon, Brazil) sites. MCS drafts behave qualitatively similar to previous aircraft and RWP cloud summaries. The Oklahoma MCSs indicate larger and more intense convective up- and downdraft cores, and greater mass flux than Amazon MCS counterparts. However, similar size-intensity relationships and draft vertical profile behaviors are observed for both regions. Additional similarities include weak positive correlations between core intensity and core width (correlation coefficient r ~ 0.5), and increases in draft intensity with altitude. A model-observational intercomparison for draft properties (core width, intensity, mass flux) is also performed to illustrate the potential usefulness of statistical observed draft characterizations. Idealized simulations with the Weather Research and Forecasting model aligned with midlatitude MCS conditions are performed at model grid spacings (?x) that range from 4 km to 250 m. It is shown that the simulations performed at ?x = 250 m at similar mature MCS lifecycle stages are those that exhibit draft intensity, width, mass flux, and shape parameter performances best matching with observed properties.

radar, arm, wind profiler, goamazon↗
NASA NTRSDate not supplied

Simulations of X-59 Sonic Thumps and Traditional Sonic Booms Propagated Around the World for Three Atmospheric Models

Propagation simulations of sonic booms from supersonic aircraft through atmospheric data over time at fixed locations provides the opportunity to assess noise exposure statistics for different climate regions. Knowledge of climate-based differences in sonic boom noise exposure statistics is important to ensure that future civil supersonic aircraft noise certification standards are globally applicable and effective. In this presentation, simulated sonic booms from the NASA X-59 Quesst quiet supersonic aircraft and conventional supersonic aircraft were propagated through atmospheric data at 100 locations across the world using PCBoom. Noise exposure statistics are compared for propagation results from three different atmospheric databases (NOAA Global Forecast System, NOAA Climate Forecast System Version 2, and the ECMWF Reanalysis Version 5). These atmospheric models were chosen due to their global coverage, popularity, and database availability. Preliminary statistical models are fit to assess the impact of several factors including flight direction, season, ground elevation, and climate on noise exposure size and loudness. Areas with prevalence of higher noise due to their climate are identified, which could help inform future supersonic aircraft noise standards.

X-59↗
DOE OSTI2024

Deep Learning-Based Weather-Related Power Outage Prediction with Socio-Economic and Power Infrastructure Data

This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory. Two distinct deep learning models, conditional Multi-Layer Perceptron (MLP) and unconditional MLP, were developed to forecast power outage probabilities, leveraging a rich array of input features gathered from publicly available sources including weather data, weather station locations, power infrastructure maps, socio-economic and demographic statistics, and power outage records. Given a one-hour-ahead weather forecast, the models predict the power outage probability for each census tract, taking into account both the weather prediction and the location's characteristics. The deep learning models employed different loss functions to optimize prediction performance. Our experimental results underscore the significance of socio-economic factors in enhancing the accuracy of power outage predictions at the census tract level.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Mapping Incidence and Prevalence Peak Data for SIR Modeling Applications

Infectious disease modeling and forecasting have played a key role in helping assess and respond to epidemics and pandemics. Recent work has leveraged data on disease peak infection and peak hospital incidence to fit compartmental models for the purpose of forecasting and describing the dynamics of a disease outbreak. Incorporating these data can greatly stabilize a compartmental model fit on early observations, where slight perturbations in the data may lead to model fits that forecast wildly unrealistic peak infection. We introduce a new method for incorporating historic data on the value and time of peak incidence of hospitalization into the fit for a Susceptible-Infectious-Recovered (SIR) model by formulating the relationship between an SIR model’s starting parameters and peak incidence as a system of two equations that can be solved computationally. We demonstrate how to calculate SIR parameter estimates – which describe disease dynamics such as transmission and recovery rates – using this method, and determine that there is a noticeable loss in accuracy whenever prevalence data is misspecified as incidence data. To exhibit the modeling potential, we update the Dirichlet-Beta State Space modeling framework to use hospital incidence data, as this framework was previously formulated to incorporate only data on total infections. This approach is assessed for practicality in terms of accuracy and speed of computation via simulation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

Evaluation of Mesoscale Model Phenomenological Verification Techniques

Forecasters at the Spaceflight Meteorology Group, 45th Weather Squadron, and National Weather Service in Melbourne, FL use mesoscale numerical weather prediction model output in creating their operational forecasts. These models aid in forecasting weather phenomena that could compromise the safety of launch, landing, and daily ground operations and must produce reasonable weather forecasts in order for their output to be useful in operations. Considering the importance of model forecasts to operations, their accuracy in forecasting critical weather phenomena must be verified to determine their usefulness. The currently-used traditional verification techniques involve an objective point-by-point comparison of model output and observations valid at the same time and location. The resulting statistics can unfairly penalize high-resolution models that make realistic forecasts of a certain phenomena, but are offset from the observations in small time and/or space increments. Manual subjective verification can provide a more valid representation of model performance, but is time-consuming and prone to personal biases. An objective technique that verifies specific meteorological phenomena, much in the way a human would in a subjective evaluation, would likely produce a more realistic assessment of model performance. Such techniques are being developed in the research community. The Applied Meteorology Unit (AMU) was tasked to conduct a literature search to identify phenomenological verification techniques being developed, determine if any are ready to use operationally, and outline the steps needed to implement any operationally-ready techniques into the Advanced Weather Information Processing System (AWIPS). The AMU conducted a search of all literature on the topic of phenomenological-based mesoscale model verification techniques and found 10 different techniques in various stages of development. Six of the techniques were developed to verify precipitation forecasts, one to verify sea breeze forecasts, and three were capable of verifying several phenomena. The AMU also determined the feasibility of transitioning each technique into operations and rated the operational capability of each technique on a subjective 1-10 scale: (1) 1 indicates that the technique is only in the initial stages of development, (2) 2-5 indicates that the technique is still undergoing modifications and is not ready for operations, (3) 6-8 indicates a higher probability of integrating the technique into AWIPS with code modifications, and (4) 9-10 indicates that the technique was created for AWIPS and is ready for implementation. Eight of the techniques were assigned a rating of 5 or below. The other two received ratings of 6 and 7, and none of the techniques a rating of 9-10. At the current time, there are no phenomenological model verification techniques ready for operational use. However, several of the techniques described in this report may become viable techniques in the future and should be monitored for updates in the literature. The desire to use a phenomenological verification technique is widespread in the modeling community, and it is likely that other techniques besides those described herein are being developed, but the work has not yet been published. Therefore, the AMIU recommends that the literature continue to be monitored for updates to the techniques described in this report and for new techniques being developed whose results have not yet been published. 111

Lambert, Winifred↗
NASA NTRS2010

Statistical Analysis of Model Data for Operational Space Launch Weather Support at Kennedy Space Center and Cape Canaveral Air Force Station

The 12-km resolution North American Mesoscale (NAM) model (MesoNAM) is used by the 45th Weather Squadron (45 WS) Launch Weather Officers at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to support space launch weather operations. The 45 WS tasked the Applied Meteorology Unit to conduct an objective statistics-based analysis of MesoNAM output compared to wind tower mesonet observations and then develop a an operational tool to display the results. The National Centers for Environmental Prediction began running the current version of the MesoNAM in mid-August 2006. The period of record for the dataset was 1 September 2006 - 31 January 2010. The AMU evaluated MesoNAM hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The MesoNAM forecast winds, temperature and dew point were compared to the observed values of these parameters from the sensors in the KSC/CCAFS wind tower network. The data sets were stratified by model initialization time, month and onshore/offshore flow for each wind tower. Statistics computed included bias (mean difference), standard deviation of the bias, root mean square error (RMSE) and a hypothesis test for bias = O. Twelve wind towers located in close proximity to key launch complexes were used for the statistical analysis with the sensors on the towers positioned at varying heights to include 6 ft, 30 ft, 54 ft, 60 ft, 90 ft, 162 ft, 204 ft and 230 ft depending on the launch vehicle and associated weather launch commit criteria being evaluated. These twelve wind towers support activities for the Space Shuttle (launch and landing), Delta IV, Atlas V and Falcon 9 launch vehicles. For all twelve towers, the results indicate a diurnal signal in the bias of temperature (T) and weaker but discernable diurnal signal in the bias of dewpoint temperature (T(sub d)) in the MesoNAM forecasts. Also, the standard deviation of the bias and RMSE of T, T(sub d), wind speed and wind direction indicated the model error increased with the forecast period all four parameters. The hypothesis testing uses statistics to determine the probability that a given hypothesis is true. The goal of using the hypothesis test was to determine if the model bias of any of the parameters assessed throughout the model forecast period was statistically zero. For th is dataset, if this test produced a value >= -1 .96 or <= 1.96 for a data point, then the bias at that point was effectively zero and the model forecast for that point was considered to have no error. A graphical user interface (GUI) was developed so the 45 WS would have an operational tool at their disposal that would be easy to navigate among the multiple stratifications of information to include tower locations, month, model initialization times, sensor heights and onshore/offshore flow. The AMU developed the GUI using HyperText Markup Language (HTML) so the tool could be used in most popular web browsers with computers running different operating systems such as Microsoft Windows and Linux.

Bauman, William H., III↗
NASA NTRS2021

Assimilation of Lidar Planetary Boundary Layer Height Observations

Lidar backscatter and wind retrievals of the planetary boundary layer height (PBLH) are assimilated into 22 hourly forecasts from the NASA Unified - Weather and Research Forecast (NU-WRF) model during the Plains Elevated Convection Convection at Night (PECAN) campaign on July 11, 2015 in Greensburg, Kansas, using error statistics collected from the model profiles to compute the necessary covariance matrices. Two separate forecast runs using different PBL physics schemes were employed, and comparisons with 6 independent radiosonde profiles were made for each run. Both of the forecast runs accurately predicted the PBLH and the state variable profiles within the planetary boundary layer during the early morning, and the assimilation had a small impact during this time. In the late afternoon, the forecast runs showed decreased accuracy as the convective boundary layer developed. However, assimilation of the Doppler lidar PBLH observations were found to improve the temperature and V velocity profiles relative to independent radiosonde profiles. Water vapor was overcorrected, leading to increased differences with independent data. Errors in the U velocity were made slightly larger. The computed forecast error covariances between the PBLH and state variables were found to rise in the late afternoon, leading to the larger improvements in the afternoon. This work represents the first effort to assimilate PBLH into forecast states using ensemble methods.

Andrew Tangborn↗
NASA NTRS1999

Parallelization of the Physical-Space Statistical Analysis System (PSAS)

Atmospheric data assimilation is a method of combining observations with model forecasts to produce a more accurate description of the atmosphere than the observations or forecast alone can provide. Data assimilation plays an increasingly important role in the study of climate and atmospheric chemistry. The NASA Data Assimilation Office (DAO) has developed the Goddard Earth Observing System Data Assimilation System (GEOS DAS) to create assimilated datasets. The core computational components of the GEOS DAS include the GEOS General Circulation Model (GCM) and the Physical-space Statistical Analysis System (PSAS). The need for timely validation of scientific enhancements to the data assimilation system poses computational demands that are best met by distributed parallel software. PSAS is implemented in Fortran 90 using object-based design principles. The analysis portions of the code solve two equations. The first of these is the "innovation" equation, which is solved on the unstructured observation grid using a preconditioned conjugate gradient (CG) method. The "analysis" equation is a transformation from the observation grid back to a structured grid, and is solved by a direct matrix-vector multiplication. Use of a factored-operator formulation reduces the computational complexity of both the CG solver and the matrix-vector multiplication, rendering the matrix-vector multiplications as a successive product of operators on a vector. Sparsity is introduced to these operators by partitioning the observations using an icosahedral decomposition scheme. PSAS builds a large (approx. 128MB) run-time database of parameters used in the calculation of these operators. Implementing a message passing parallel computing paradigm into an existing yet developing computational system as complex as PSAS is nontrivial. One of the technical challenges is balancing the requirements for computational reproducibility with the need for high performance. The problem of computational reproducibility is well known in the parallel computing community. It is a requirement that the parallel code perform calculations in a fashion that will yield identical results on different configurations of processing elements on the same platform. In some cases this problem can be solved by sacrificing performance. Meeting this requirement and still achieving high performance is very difficult. Topics to be discussed include: current PSAS design and parallelization strategy; reproducibility issues; load balance vs. database memory demands, possible solutions to these problems.

Larson, J. W.↗
NASA NTRS2018

Sub-Seasonal Forecasting of the Stratospheric Wave Events, Sudden Stratospheric Warmings, and Their Influence on the Troposphere

Stratospheric wave events and major sudden stratospheric warming (SSW) events are well captured in high-resolution global forecasts out to 10 days. Tropospheric influences of SSW include statistically significant shifts in the storm tracks and associated surface temperature and precipitation pattern changes. Since these events can in turn influence the troposphere on time scales of 30-60 days the ability to predict these events and the subsequent long-term response on time scales beyond 10 days is of interest. Here we examine the prediction of stratospheric wave events and their evolution using the NASA GMAO (Global Modeling and Assimilation Office) Sub-Seasonal to Seasonal (S2S) system. This is a recently released subseasonal to seasonal forecast system, GEOS-S2S version 2.1. Compared to GMAO's previous system, the new version runs at higher atmospheric resolution (approximately 1/2 degree globally), contains a substantially improved model of the cryosphere, includes additional interactive aerosol model components, and the ocean data assimilation system has been replaced with a Local Ensemble Transform Kalman Filter. Results are based on a comprehensive series of hindcasts starting from the year 2000. They show that while the S2S system is not as accurate at 10 days in forecasting major SSW events as the NASA GMAO Forward Processing system, it can usefully predict stratospheric anomalies out to 20 days and the subsequent stratospheric/tropospheric evolution beyond 30 days.

Coy, L.↗
DOE OSTI2023

From Optimization to Sampling Through Gradient Flows

Optimization and sampling algorithms play a central role in science and engineering as they enable finding optimal predictions, policies, and recommendations, as well as expected and equilibrium states of complex systems. The notion of “optimality” is formalized by the choice of an objective function, while the notion of an “expected” state is specified by a probabilistic model for the distribution of states. Optimizing rugged objective functions and sampling multimodal distributions is computationally challenging, especially in high-dimensional problems. Here, for this reason, many optimization and sampling methods have been developed by researchers working in disparate fields such as Bayesian statistics, molecular dynamics, genetics, quantum chemistry, machine learning, weather forecasting, econometrics, and medical imaging.

Trillos, N. García↗
DOE OSTI2024

Model orthogonalization and Bayesian forecast mixing via principal component analysis

One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework. In many cases, however, the models used in the mixing process are similar. In addition to contaminating the model space, the existence of such similar, or even redundant, models during the multimodeling process can result in misinterpretation of results and deterioration of predictive performance. In this paper we describe a method based on the principal component analysis that eliminates model redundancy. We show that by adding model orthogonalization to the proposed Bayesian model combination framework, one can arrive at better prediction accuracy and reach excellent uncertainty quantification performance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1999

The GEOS Ozone Data Assimilation System: Design and Validation

An ozone data assimilation system has been developed at the Data Assimilation Office of the NASA/Goddard Space Flight Center to provide global three-dimensional analyzed ozone mixing ratio and total column ozone. The Total Ozone Mapping Spectrometer (TOMS) total column ozone and the Solar Backscatter Ultraviolet (SBUV) or SBUV/2 partial ozone profile data are assimilated. The analyzed winds from the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) drive the ozone transport. Following every transport model timestep, the model prediction is combined with the observations using a global, physical-space based, statistical analysis scheme. Due to the smaller size of the ozone system than that of a global meteorological data assimilation system, new statistical analysis methodology, including anisotropic and flow-dependent forecast error correlation models, can be implemented and tested in the ozone system more easily. Sample results from the winter 1992 validation period are presented. There is a close agreement between the analyzed fields and the independent observations from ozone sondes and the Halogen Occultation Experiment (HALOE).

Stajner, I.↗
NASA NTRS2015

Assimilation of GPM GMI Rainfall Product with WRF GSI

The Global Precipitation Measurement (GPM) is an international mission to provide next-generation observations of rain and snow worldwide. The GPM built on Tropical Rainfall Measuring Mission (TRMM) legacy, while the core observatory will extend the observations to higher latitudes. The GPM observations can help advance our understanding of precipitation microphysics and storm structures. Launched on February 27th, 2014, the GPM core observatory is carrying advanced instruments that can be used to quantify when, where, and how much it rains or snows around the world. Therefore, the use of GPM data in numerical modeling work is a new area and will have a broad impact in both research and operational communities. The goal of this research is to examine the methodology of assimilation of the GPM retrieved products. The data assimilation system used in this study is the community Gridpoint Statistical Interpolation (GSI) system for the Weather Research and Forecasting (WRF) model developed by the Development Testbed Center (DTC). The community GSI system runs in independently environment, yet works functionally equivalent to operational centers. With collaboration with the NASA Short-term Prediction Research and Transition (SPoRT) Center, this research explores regional assimilation of the GPM products with case studies. Our presentation will highlight our recent effort on the assimilation of the GPM product 2AGPROFGMI, the retrieved Microwave Imager (GMI) rainfall rate data for initializing a real convective storm. WRF model simulations and storm scale data assimilation experiments will be examined, emphasizing both model initialization and short-term forecast of precipitation fields and processes. In addition, discussion will be provided on the development of enhanced assimilation procedures in the GSI system with respect to other GPM products. Further details of the methodology of data assimilation, preliminary result and test on the impact of GPM data and the influence on precipitation forecast will be presented at the conference.

Li, Xuanli↗