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NASA NTRS2015

A WRF-Chem Analysis of Flash Rates, Lightning-NOx Production and Subsequent Trace Gas Chemistry of the 29-30 May 2012 Convective Event in Oklahoma During DC3

The Deep Convective Clouds and Chemistry (DC3) field campaign in 2012 provided a plethora of aircraft and ground-based observations (e.g., trace gases, lightning and radar) to study deep convective storms, their convective transport of trace gases, and associated lightning occurrence and production of nitrogen oxides (NOx). This is a continuation of previous work, which compared lightning observations (Oklahoma Lightning Mapping Array and National Lightning Detection Network) with flashes generated by various flash rate parameterization schemes (FRPSs) from the literature in a Weather Research and Forecasting Chemistry (WRF-Chem) model simulation of the 29-30 May 2012 Oklahoma thunderstorm. Based on the Oklahoma radar observations and Lightning Mapping Array data, new FRPSs are being generated and incorporated into the model. The focus of this analysis is on estimating the amount of lightning-generated nitrogen oxides (LNOx) produced per flash in this storm through a series of model simulations using different production per flash assumptions and comparisons with DC3 aircraft anvil observations. The result of this analysis will be compared with previously studied mid-latitude storms. Additional model simulations are conducted to investigate the upper troposphere transport, distribution, and chemistry of the LNOx plume during the 24 hours following the convective event to investigate ozone production. These model-simulated mixing ratios are compared against the aircraft observations made on 30 May over the southern Appalachians.

Cummings, Kristin A.↗

DOE OSTI2024

Anticipating Optical Availability in Hybrid RF/FSO Links Using RF Beacons and Deep Learning

Radiofrequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates for space communications. Accurate prediction of dynamic ground-to-satellite FSO link availability is critical for routing decisions in low-earth orbit constellations. In this paper, we propose a system leveraging ubiquitous RF links to proactively forecast FSO link degradation prior to signal drops below threshold levels. This enables pre-calculation of rerouting to maximally maintain high data rate FSO links throughout the duration of weather effects. We implement a supervised learning model to anticipate FSO attenuation based on the analysis of RF patterns. Through the simulation of a dense lower earth orbit (LEO) satellite constellation, we demonstrate the efficacy of our approach in a simulated satellite network, highlighting the balance between predictive accuracy and prediction duration. An emulated cloud attenuation model is proposed to provide insight into the temporal profiles of RF signals and their correlation to FSO channel dynamics. Our investigation sheds light on the trade-offs between prediction horizon and accuracy arising from RF beacon numbers and proximity.

FSO availability↗

NASA NTRS1980

An automatic locating system for cloud-to-ground lightning

Automatic locating systems which respond to cloud to ground lightning and which discriminate against cloud discharges and background noise are described. Subsystems of the locating system, which include the direction finder and the position analyzer, are discussed. The direction finder senses the electromagnetic fields radiated by lightning on two orthogonal magnetic loop antennas and on a flat plate electric antenna. The position analyzer is a preprogrammed microcomputer system which automatically computes, maps, and records lightning locations in real time using data inputs from the direction finder. The use of the locating systems for wildfire management and fire weather forecasting is discussed.

Krider, E. P.↗

NASA NTRS2000

Satellite Mapping of the Earth's Ozone and Sulfur Dioxide

The Total Ozone Mapping Spectrometer (TOMS) instruments are spatially-scanning UV spectrometers that have produced daily global images of total ozone over the last 21 years since the launch of the Nimbus 7 satellite. The instruments use a total ozone retrieval algorithm pioneered by J.V. Dave and C. L. Mateer for the Nimbus 4 Backscatter Ultraviolet (BUV) instrument, designed by D.F. Heath. The TOMS ozone maps have revealed the relations between total ozone and atmospheric dynamics, and shown the dramatic losses of ozone in the Antarctic ozone hole and the Northern hemisphere. The accepted long-term trends in global, regional, and local ozone are derived from data from the Nimbus 7 TOMS and three successive TOMS flights on Russian, Japanese, and American satellites. The next TOMS flight will be launched in 2000. The contiguous mapping design and fortuitous choice of TOMS wavelengths bands also permitted imaging of a second atmospheric gas, sulfur dioxide, which is transient due to its short lifetime. The importance of this measurement was first realized after the eruption of El Chichon volcano in 1982. The extreme range of sizes of volcanic eruptions and the 'associated danger require observations from a distant observing platform. The first quantitative time series of the input of sulfur dioxide by explosive volcanic eruptions into the atmosphere thus was developed from the TOMS missions. Finally, the Rayleigh and aerosol scattering spectral characteristic and reflectivity complete the four dominant pieces of information in the near UV albedo of the Earth. The four parameters are derived with a linear algorithm, the absorption coefficients of the gases, and effective paths computed from radiative transfer tables. Absorbing aerosol clouds (smoke, dust, volcanic ash) are readily identified by their deviation from a Rayleigh signature. The greatest shortcoming of the TOMS dataset is the 24 hour time resolution that is produced by the polar orbit of the satellite. Dynamic phenomena, such as upper air fronts that modulate total ozone and volcanic eruptions of sulfur dioxide and ash, cannot be adequately resolved. It is hoped that UV observations from geostationary satellites will soon be made to test the value of this unique information in weather forecasting and aviation safety.

Krueger, Arlin↗

NASA NTRS2000

Satellite Mapping of the Earth's Ozone and Sulfur Dioxide

The Total Ozone Mapping Spectrometer (TOMS) instruments are spatially-scanning UV spectrometers that have produced daily global images of total ozone over the last 21 years since the launch of the Nimbus 7 satellite. The instruments use a total ozone retrieval algorithm pioneered by J.V. Dave and C. L. Mateer for the Nimbus 4 Backscatter Ultraviolet (BUV) instrument, designed by D.F. Heath. The TOMS ozone maps have revealed the relations between total ozone and atmospheric dynamics, and shown the dramatic losses of ozone in the Antarctic ozone hole and the Northern hemisphere. The accepted long-term trends in global, regional, and local ozone are derived from data from the Nimbus 7 TOMS and three successive TOMS flights on Russian, Japanese, and American satellites. The next TOMS flight will be launched in 2000. The contiguous mapping design and fortuitous choice of TOMS wavelengths bands also permitted imaging of a second atmospheric gas, sulfur dioxide, which is transient due to its short lifetime. The importance of this measurement was first realized after the eruption of El Chichon volcano in 1982. The extreme range of sizes of volcanic eruptions and the associated danger require observations from a distant observing platform. The first quantitative time series of the input of sulfur dioxide by explosive volcanic eruptions into the atmosphere thus was developed from the TOMS missions. Finally, the Rayleigh and aerosol scattering spectral characteristic and reflectivity complete the four dominant pieces of information in the near UV albedo of the Earth. The four parameters are derived with a linear algorithm, the absorption coefficients of the gases, and effective paths computed from radiative transfer tables. Absorbing aerosol clouds (smoke, dust, volcanic ash) are readily identified by their deviation from a Rayleigh signature. The greatest shortcoming of the TOMS dataset is the 24 hour time resolution that is produced by the polar orbit of the satellite. Dynamic phenomena, such as upper air fronts that modulate total ozone and volcanic eruptions of sulfur dioxide and ash, cannot be adequately resolved. It is hoped that UV observations from geostationary satellites will soon be made to test the value of this unique information in weather forecasting and aviation safety.

Krueger, Arlin↗

DOE OSTI2024

Geometry of Non-Diffusive Tracer Transport in Gridded Atmospheric Models

A first-order linear and numerically non-diffusive Eulerian transport algorithm, minVAR, was recently developed for preservation of correlations between interrelated tracers during advective transport. The present study extends this work by: (1) providing further investigation of several interesting geometric constructions found in contours of constant minVAR, short for minimum variance, through extension to three coordinate dimensions. These contours capture point-by-point representations of thousands of individual atmospheric aerosol and/or cloud particles as they evolve and are rendered on Eulerian grids at a level of sub-grid resolution limited only by numerical precision; and (2) exploration of geometric similarities between the Arakawa C-grid, used to obtain interpolated values of the wind field at grid scale and minVAR. In particular, we consider interpolation of the u and v horizontal components of wind velocity from grid to sub-grid scales. The last results are motivated by recent applications of the Weather Research and Forecasting (WRF) model applied in the coastal Houston region, where the recent TRacking Aerosol Convection Interactions ExpeRiment (TRACER) field campaign was organized. A unique and fully consistent mapping is obtained between particles moving along meteorological wind trajectories and the non-diffusive, non-dispersive representation of such trajectories on an Eulerian grid.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Evaluation of a cloudy cold-air pool in the Columbia River basin in different versions of the High-Resolution Rapid Refresh (HRRR) model

The accurate forecast of persistent orographic cold-air pools in numerical weather prediction models is essential for the optimal integration of wind energy into the electrical grid during these events. Model development efforts during the second Wind Forecast Improvement Project (WFIP2) aimed to address the challenges related to this. We evaluated three versions of the National Oceanic and Atmospheric Administration (NOAA) High-Resolution Rapid Refresh model with two different horizontal grid spacings against in situ and remote sensing observations to investigate how developments in physical parameterizations and numerical methods targeted during WFIP2 impacted the simulation of a persistent cold-air pool in the Columbia River basin. Differences amongst model versions were most apparent in simulated temperature and low-level cloud fields during the persistent phase of the cold-air pool. The model developments led to an enhanced low-level cloud cover, resulting in better agreement with the observations. This removed a diurnal cycle in the near-surface temperature bias at stations throughout the basin by reducing a cold bias during the night and a warm bias during the day. However, low-level clouds did not clear sufficiently during daytime in the newest model version, which leaves room for further model developments. The model developments also led to a better representation of the decay of the cold-air pool by slowing down its erosion.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon

The Weather Research and Forecasting (WRF) model can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations but subjected to uncertainties associated with model physical parameterizations. In this study, we propose a framework to quantify and reduce model physical parameterization uncertainties associated with surface fluxes and L-A coupling. An ensemble of WRF simulations with different physical schemes is used to simulate surface fluxes and land-atmosphere coupling strength over the Amazon region. The physical parameterizations investigated include cloud microphysics (MP), land surface processes (LSM), planetary boundary layer (PBL), surface layer (SL), and cumulus (CU). We perform 120 ensemble simulations using the WRF model and different combinations of six MPs, three LSMs, six PBLs and SLs and three CUs. The measurements from the GoAMAZON field campaign and satellite data are used to evaluate model performance. A Multi-way analysis of variance (ANOVA) approach is applied to quantify the relative importance of different physics processes on L-A coupling. The Tukey’s test is used to sort schemes that have no significant differences into one group. The suite of physics that result in the best simulations of the corresponding variables are selected based on the Taylor skill score. Results show that the relative importance of processes and their interaction vary with the variables of interest. For example, CU was the most important process in modulating soil moisture, 2 m-humidity, latent heat, and net radiation. LSM showed dominant effects on 2 m-temperature and also has the largest impact on sensible heat and the lifting condensation level. The best physical parameterization ensembles show much narrower ranges of the variables of interest than the priori ensemble. Results of this study show the roles of different physical processes in modulating L-A interactions, quantify model uncertainties from physical processes, and provide insights for improving the model physics parameterizations.

Wang, Chen↗

DOE OSTI2025

High sensitivity of simulated fog properties to parameterized aerosol activation in case studies from ParisFog

Aerosols influence fog properties such as visibility and lifetime by affecting fog droplet number concentrations (N d ). Numerical weather prediction (NWP) models often represent aerosol–fog interactions using highly simplified approaches. Incorporating prognostic size-resolved aerosol microphysics from climate models could allow them to simulate N d and aerosol–fog interactions without incurring excessive computational expense. However, microphysics code designed for coarse spatial resolution may struggle with sub-kilometer-scale grid spacings. Here, we test the ability of the UK Met Office Unified Model to simulate aerosol and fog properties during case studies from the ParisFog field campaign in 2011. We examine the sensitivity of fog properties to variations in N d caused by modifications to simulated aerosol activation. Our model, with a 500 m horizontal resolution and interactive aerosol and cloud microphysics, significantly underpredicts N d , although it only slightly underestimates the cloud condensation nuclei concentration. With an updated version of the Abdul-Razzak and Ghan (2000) activation scheme, we produce N d that are more consistent with those predicted by a cloud parcel model under fog-like conditions. We activate droplets only by adiabatic cooling. We incorporate more realistic hygroscopicities for sulfate and organic aerosols and explore the sensitivity of simulated N d to unresolved updrafts. We find that both N d and simulated fog liquid water content are very sensitive to the updated activation scheme but remain less affected by the update to hygroscopicities. Our improvements offer insights into the physical processes regulating N d in stable conditions, potentially laying foundations for improved operational fog forecasts that incorporate interactive aerosol simulations or aerosol climatologies.

Ghosh, Pratapaditya [Carnegie Mellon University, P↗

NASA NTRS1994

Tropical impacts of SST forcing: A case study for 1987 versus 1988

The response of the NASA/Goddard Institute for Space Studies general circulation model (GCM) to large tropical sea surface temperature (SST) anomalies is investigated by evaluating model simulations of the particularly contrasting summer monsoon seasons 1987 and 1988. These years are representative of the warm and cold phases, respectively, of a recent El Nino-Southern Oscillation (ENSO) event. An ensemble averaging the results of three simulations was considered for each season, using monthly mean observed SST anomalies for June-August 1987 and 1988 as lower boundary forcing. Consistent with the European Center for Medium Weather Forecast (ECMWF)-analyzed winds, the simulations based on 1988 as compared to 1987 SST exhibit stronger upper-tropospheric irrotational circulation between the monsoon regions and the Southern Hemispheric sub-tropical anticyclones, a stronger Pacific Walker cell and a weaker subtropical westerly jet over the South Pacific. In the same vein, the modeled precipitation, indicating a more northerly position of the Pacific Inter-Tropical Convergence Zone (ITCZ) in 1988 compared with 1987, is supported by satellite observations of outgoing longwave radiation and highly reflective clouds.

Druyan, Leonard M.↗

NASA NTRS2009

Data Assimilation Experiments Using Quality Controlled AIRS Version 5 Temperature Soundings

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains a number of significant improvements over Version 4. Two very significant improvements are described briefly below. 1) The AIRS Science Team Radiative Transfer Algorithm (RTA) has now been upgraded to accurately account for effects of non-local thermodynamic equilibrium on the AIRS observations. This allows for use of AIRS observations in the entire 4.3 micron CO2 absorption band in the retrieval algorithm during both day and night. Following theoretical considerations, tropospheric temperature profile information is obtained almost exclusively from clear column radiances in the 4.3 micron CO2 band in the AIRS Version 5 temperature profile retrieval step. These clear column radiances are a derived product that are indicative of radiances AIRS channels would have seen if the field of view were completely clear. Clear column radiances for all channels are determined using tropospheric sounding 15 micron CO2 observations. This approach allows for the generation of accurate values of clear column radiances and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel clear column radiances. These error estimates are used for quality control of the retrieved products. Based on error estimate thresholds, each temperature profiles is assigned a characteristic pressure, pg, down to which the profile is characterized as good for use for data assimilation purposes. We have conducted forecast impact experiments assimilating AIRS quality controlled temperature profiles using the NASA GEOS-5 data assimilation system, consisting of the NCEP GSI analysis coupled with the NASA FVGCM, at a spatial resolution of 0.5 deg by 0.5 deg. Assimilation of Quality Controlled AIRS temperature profiles down to pg resulted in significantly improved forecast skill compared to that obtained from experiments when all data used operationally by NCEP, except for AIRS data, is assimilated. These forecasts were also significantly better than to those obtained when AIRS radiances (rather than temperature profiles) are assimilated, which is the way AIRS data is used operationally by NCEP and ECMWF.

Susskind, Joel↗

NASA NTRS2021

Southern Idaho Health & Air Quality II: Evaluating Atmospheric Mixing Height Estimations in the Western United States

Wildfires in the western United States have caused immense infrastructure damage and loss of human life in recent years. Wildfire smoke, which travels far from its original source, is also harmful to human health. Mixing height, which acts as a lid and prevents smoke from rising above a certain altitude in the lower troposphere, is a critical input in smoke dispersion and air quality models used by agencies that monitor wildfires. These models, coupled with forecaster expertise, are also used to decide when it is safe to execute a prescribed burn. The DEVELOPID team partnered with the National Weather Service (NWS) Fire Weather Program, Bureau of Land Management (BLM), National Interagency Fire Center (NIFC), and National Park Service (NPS) Fire Management Program Center (FMPC) to help improve reliability and confidence in mixing height estimations, and therefore the burn prescription decision-making process. To that end, the team developed a toolbox for measuring smoke-related aerosol mixing heights using Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observations (CALIPSO) Vertical Feature Mask granules. CALIPSO mixing heights and NWS estimations covaried significantly and positively. However, substantial disagreement between the methods stymied the team’s attempts to quantify systematic bias in a meaningful way. The relative error between the methods was especially large at low mixing heights, which suggests that this method of validation may only be suitable at higher altitudes.

Dean Berkowitz↗

NASA NTRSDate not supplied

Improving the Use of Surface-Sensitive Radiances in the GMAO Hybrid-4DEnVar System

The planetary boundary layer (PBL) was designated as an incubation-class targeted observable (TO) in the 2018 Decadal Survey. As no single instrument source will provide enough information to constrain the global PBL, the assimilation of a wide range of observations in data assimilation (DA) systems will play a critical role, and GMAO has put in efforts to enhance surface-sensitive radiance assimilation. Although a vast number of microwave radiance observations are used in the GEOS global DA system, very few surface-sensitive radiances are currently used over land due to large uncertainty of land surface emissivity in the CRTM as well as cloud detection issue. Dynamically varying emissivity is retrieved from observations of window channels in the GEOS system for non-scattering FOVs and applied to sounding channel assimilation. Moreover, the original radiance bias correction is found not to work well over land, and shows drifting bias when the original emissivity sensitivity bias predictor with a dynamically varying emissivity retrieval is used. Hence, the radiance bias correction has been modified, and the quality control procedure has been adapted accordingly. Cycled experiment results show improvement in the temperature forecast at lower model levels; further refinement of this system continues.

Yanqiu Zhu↗

NASA NTRS2008

The NASA Energy and Water Cycle Extreme (NEWSE) Integration Project

Skillful predictions of water and energy cycle extremes (flood and drought) are elusive. To better understand the mechanisms responsible for water and energy extremes, and to make decisive progress in predicting these extremes, the collaborative NASA Energy and Water cycle Extremes (NEWSE) Integration Project, is studying these extremes in the U.S. Southern Great Plains (SGP) during 2006-2007, including their relationships with continental and global scale processes, and assessment of their predictability on multiple space and time scales. It is our hypothesis that an integrative analysis of observed extremes which reflects the current understanding of the role of SST and soil moisture variability influences on atmospheric heating and forcing of planetary waves, incorporating recently available global and regional hydro- meteorological datasets (i.e., precipitation, water vapor, clouds, etc.) in conjunction with advances in data assimilation, can lead to new insights into the factors that lead to persistent drought and flooding. We will show initial results of this project, whose goals are to provide an improved definition, attribution and prediction on sub-seasonal to interannual time scales, improved understanding of the mechanisms of decadal drought and its predictability, including the impacts of SST variability and deep soil moisture variability, and improved monitoring/attributions, with transition to applications; a bridging of the gap between hydrological forecasts and stakeholders (utilization of probabilistic forecasts, education, forecast interpretation for different sectors, assessment of uncertainties for different sectors, etc.).

House, P. R.↗

DOE OSTI2024

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Assimilation of Dual-Polarimetric Radar Observations with WRF GSI

Dual-polarimetric (dual-pol) radar typically transmits both horizontally and vertically polarized radio wave pulses. From the two different reflected power returns, more accurate estimate of liquid and solid cloud and precipitation can be provided. The upgrade of the traditional NWS WSR-88D radar to include dual-pol capabilities will soon be completed for the entire NEXRAD network. Therefore, the use of dual-pol radar network will have a broad impact in both research and operational communities. The assimilation of dual-pol radar data is especially challenging as few guidelines have been provided by previous research. It is our goal to examine how to best use dual-pol radar data to improve forecast of severe storm and forecast initialization. In recent years, the Development Testbed Center (DTC) has released the community Gridpoint Statistical Interpolation (GSI) DA system for the Weather Research and Forecasting (WRF) model. 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 study explores regional assimilation of the dual-pol radar variables from the WSR-88D radars for real case storms. Our presentation will highlight our recent effort on incorporating the horizontal reflectivity (ZH), differential reflectivity (ZDR), specific differential phase (KDP), and radial velocity (VR) data for initializing convective storms, with a significant focus being on an improved representation of hydrometeor fields. In addition, discussion will be provided on the development of enhanced assimilation procedures in the GSI system with respect to dual-pol variables. Beyond the dual-pol variable assimilation procedure developing within a GSI framework, highresolution (≤1 km) 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. Further details of the methodology of data assimilation, the impact of different dual-pol variables, the influence on precipitation forecast will be presented at the conference.

Li, Xuanli↗

NASA NTRS2014

Assimilation of Dual-Polarimetric Radar Observations with WRF GSI

Li, Xuanli↗

NASA NTRS2013

Assessing the Impact of Pre-gpm Microwave Precipitation Observations in the Goddard WRF Ensemble Data Assimilation System

The forthcoming Global Precipitation Measurement (GPM) Mission will provide next generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud-resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction (NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system. We present a data impact study on the assimilation of precipitation-affected microwave (MW) radiances from a pre-GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF-EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast (WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single-observation assimilation experiments. An empirical bias correction for precipitation-affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case-study on a convective storm also reveals that the accuracy of ensemble-based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.

microwave observations↗