Satellite Radiance Data Research Articles

A microwave temperature sounder (MWTS)–microwave humidity sounder fusion cloud detection method for land data from the FengYun‐3D MWTS and its impact on error estimation

AbstractThe presence of clouds greatly disrupting the estimation of observation errors in satellite data. Therefore, cloud detection is a necessary step in satellite radiance data assimilation, not only to identify clear sky data and cloudy data, but also, more importantly, to accurately specify observation errors in different scenarios during all‐sky data assimilation. However, the assimilation of the microwave temperature sounder‐2 (MWTS‐2) onboard the FengYun‐3D satellite has been restricted by the low accuracy of cloud detection due to the absence of low‐frequency channels, especially for land areas where complex underlying surface brings more difficulties to the identification of cloudy data. The microwave humidity sounder‐2 (MWHS‐2), which is designed for water vapor sounding, is usually equipped on the same satellite as MWTS‐2. The sensitivity of the MWHS‐2 to water vapor enables it to provide more accurate information about clouds over land. Based on the advantage of the two instruments measuring on the same platform, an effective cloud detection method for MWTS‐2 data over land is established in this study after collocating the data of the MWTS‐2 and MWHS‐2. The results show that the new method achieves a cloud detection rate exceeding 75%, with a significantly reduced false detection rate compared with the traditional scattering index method, which is decreased from 40% to approximately 26%. More importantly, in non‐precipitating cloud regions, the cloud index built by the new method has a significant linear correlation with the difference between observed and simulated brightness temperatures (OMB) of MWTS‐2 data. Statistical analysis reveals that the cloud index can be used to effectively rectify the systematic OMB bias caused by those clouds in non‐precipitation cloud areas, which demonstrates the promising application of this cloud index in all‐sky MWTS‐2 data assimilation over land.

Impact of Atmospheric River Reconnaissance Dropsonde Data on the Assimilation of Satellite Radiance Data in GFS

Abstract Satellites provide the largest dataset for monitoring the earth system and constraining analyses in numerical weather prediction models. A significant challenge for utilizing satellite radiances is the accurate estimation of their biases. High-accuracy nonradiance data are commonly employed to anchor radiance bias corrections. However, aside from the impacts of radio occultation data in the stratosphere, the influence of other types of “anchor” observation data on radiance assimilation remains unclear. This study provides an assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program, which samples ARs over the northeast Pacific, on the radiance assimilation using the Global Forecast System (GFS) and Global Data Assimilation System at the National Centers for Environmental Prediction. The assimilation of this dropsonde dataset has proven crucial for providing enhanced anchoring for bias corrections and improving the model background, leading to an increase of ∼5%–10% in the number of assimilated microwave radiance in the lower troposphere/midtroposphere over the northeast Pacific and North America. The impact on tropospheric infrared radiance is not only small but also beneficial. Impacts of dropsondes on the use of stratospheric channels are minimal due to the absence of dropsonde observations at certain altitudes, such as aircraft flight levels (e.g., 150 hPa). Results in this study underscore the usefulness of dropsondes, along with other conventional data, in optimizing the assimilation of satellite radiance. This study reinforces the importance of a diverse observing network for accurate weather forecasting and highlights the specific benefits derived from integrating dropsonde data into radiance assimilation processes. Significance Statement This study aims to evaluate the impact of aircraft reconnaissance dropsondes on the assimilation of satellite radiance data, utilizing observations from the 2020 Atmospheric River Reconnaissance program. The key findings reveal a substantial enhancement in the model first guess and improved estimates of radiance biases. Notably, there is a significant 5%–10% increase in microwave radiance observations over the northeastern Pacific and North America, with positive yet modest effects observed in tropospheric infrared radiance. These findings underscore the crucial role of atmospheric river reconnaissance dropsondes as anchor data, enhancing the assimilation of radiance observations. In essence, the inclusion of these dropsondes in routine networks is particularly valuable for optimizing data assimilation in regions with sparse observational data.

Role of assimilation of microwave humidity sounder (MHS) satellite radiance in forecast of structure and intensity of VSCS Vardah 2016

The present study evaluated the performance of the Weather Research and Forecasting (WRF) model in the forecast of a very severe cyclonic storm (VSCS) Vardah that developed over the Bay of Bengal (BoB) and make landfall over the Tamil Nadu coast on 12 December 2016. The study examined the impact of microwave humidity sounder (MHS) satellite radiance data assimilation to forecast of structure, intensity and rainfall of Vardah cyclone near to the coast using two different land surface model (LSM; Noah and Noah-MP). The mean track error is about 33 and 38 km with data assimilation using Noah and Noah-MP LSM respectively and this error is about 41 km in without data assimilation using Noah and Noah-MP LSM. The predicted intensity of the storm in terms of the maximum surface wind is also slightly better predicted in data assimilation experiment with Noah LSM, this is due to the improved initial condition. Accumulated rainfall and the maximum reflectivity in terms of spatial distribution and magnitude of the VSCS Vardah is well simulated in all the experiments but slightly better in data assimilation experiments. Hovmoller diagram is also presented to see the shifting pattern of accumulated rainfall across the region during simulation period.

The added value of Himawari-8 satellite radiance data assimilation for very heavy rainfall prediction in the Indonesian Maritime Continent (Case study: East Kalimantan)

Abstract This study evaluates the impact of assimilating Advanced Himawari Imager (AHI) radiance data on the 8-10 water vapor channel using the three-dimensional variational (3DVAR) technique for the very heavy rainfall events in East Kalimantan on June 2-4, 2019. We utilized the Weather Research and Forecasting (WRF) and WRF Data Assimilation (WRFDA) system to run assimilation and prediction model simulations. To compare the assimilated model with AHI data and without assimilation, we devised two experimental schemes: 3DVAR and NODA. We assessed the enhancement in model prediction by comparing simulation results based on convection evolution, vertically integrated moisture flux convergence (VIMFC) increment, and rain prediction skill metrics, including Threat Score (TS), Equitable Threat Score (ETS), and Fractions Skill Score (FSS). Our findings suggest that the incorporation of AHI data results in a substantial improvement of the WRF model’s prediction of the very heavy rainfall events in East Kalimantan.

Solar Radiation Gridded Satellite data comparison in Gran Canaria Island

During the last years solar energy has been increasing the percentage of the total energy production around the world and it is expected it persists growing notably [1]. In insular grids, such as Canary Islands, the knowledge of solar radiation behaviour is one of the most important variables in order to ensure the stability for power systems. In this paper we study the suitability of Gridded Satellite data for modelling solar radiation for both PV and CSP purposes. The analysis and comparison is performed using hourly Global and Direct Solar radiation from the satellite model CM SAF database and four ground measurement stations in Gran Canaria. This study could give us a better knowledge of solar radiation behavior in a wide spatial and temporal coverage. Moreover, an accurate satellite radiation data could improve solar radiation forecasting for power systems manage systems [2].

Direct Assimilation of All-Sky GOES-R ABI Radiances in GSI EnKF for the Analysis and Forecasting of a Mesoscale Convective System

Abstract In this study, all-sky GOES-R ABI infrared radiances at their native resolution are assimilated using an enhanced GSI ensemble Kalman filter (EnKF) data assimilation (DA) system, and the impacts of the data on the analysis and forecast of a mesoscale convective system (MCS) are explored. Results show that all-sky ABI BT data can correctly build up observed storms within the model and effectively remove spurious storms in model background through frequent DA cycles. Both bias and root-mean-squared innovation of the background and analysis are significantly reduced during the DA cycles, and free forecasts are improved when verified subjectively and objectively against observed ABI BTs and independent radar reflectivity observations. A horizontal localization radius of 30 km is found to produce the best results while 5-min DA cycles improve the storm analyses over 15-min cycles, but the differences in forecasts are small. Further analyses show that the clearing of spurious clouds by ABI radiance is correctly accompanied by reduction in moisture through background error cross covariance, but overdrying often occurs, which can cause spurious storm decay in the forecast. The problem is reduced when the ensemble mean of observation prior instead of observation prior of the ensemble mean state is used in the ensemble mean state update equation of EnKF. The significant difference between the two ways that the ensemble mean of observation prior is calculated when the observational operator is very nonlinear has not been recognized in earlier cloudy radiance DA studies. Significance Statement Satellite observations in cloudy regions are not used in most current operational weather prediction systems due to complex nonlinear relations between satellite-observed quantities, the radiances, and model state in such regions. The models also must predict clouds reasonably well for cloudy observations to be effectively assimilated. The latest NOAA geostationary satellites can provide radiance observations at high spatial and temporal resolutions and such data in both cloudy- and clear-air regions are assimilated using an advanced data assimilation method into a model that explicitly represents convection. Forecasts up to 4 h are improved by the assimilation while several issues associated with the assimilation are discussed. The study contributes to the eventual use of all-sky satellite radiance data in operational models.

A Machine Learning Approach to the Observation Operator for Satellite Radiance Data Assimilation

A Machine Learning Approach to the Observation Operator for Satellite Radiance Data Assimilation

Light pollution indicators for all the major astronomical observatories

ABSTRACT Light pollution at astronomical observatories is one of the main factors to be taken into account to preserve their scientific productivity and their useful lifetime. Using the Garstang–Cinzano model applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) 2021 satellite radiance data, we have compared 28 sites, all hosting telescopes with apertures larger than 3 m, plus some additional selected sites. We computed and analysed five indicators of light pollution: radiance at zenith; averaged at 60° zenith distance; averaged over all the sky; averaged in the first 10° above the horizon; and horizontal irradiance. We found large variations of the values of the indicators, with a factor greater than 600 for the zenith artificial radiance between the least and most polluted major observatories. The results show that two-thirds of all large observatories have already surpassed the critical 10 per cent increase in radiance over the assumed natural levels. The results presented and the method described here can help to plan countermeasures in order to lower the impact of light pollution on observatories. These same methods can be also used to protect the night environment from the impact of artificial light (e.g. on biodiversity, on animal behaviour and physiology, on human health).

Satellite radiance assimilation using the 3D-var technique for the heavy rainfall over the Indian region

The present study evaluates the impact of satellite radiance assimilation on the predictability of the WRF model. The 3D-VAR system of the WRF model was used to assimilate satellite radiance data at the initial condition. Four thunderstorm cases were considered, which occurred over Kolkata, India. A set of two numerical experiments, namely CNTL (no-assimilation) and SAT (satellite radiance assimilation), were carried out, and the WRF model was integrated for 24 hours. The assimilation of satellite radiance had improved the wind and moisture fields at the initial condition, which led to evolve the conducive atmosphere further. Results showed that even though thunderstorms are a small-scale system, assimilation of satellite radiance had shown significant improvement in the simulation of the location, evolution, and intensity of thunderstorms. Temporal evolution of surface parameters (such as 2-m temperature and rainfall) was closer to observation (AWS) in SAT experiment. Further, the SAT experiments were able to produce stronger updrafts/downdrafts and increased moisture content deep into the atmosphere. These findings suggest that satellite radiance data assimilation significantly impacts initial conditions, providing a conducive atmosphere.

Testing Variational Bias Correction of Satellite Radiance Data in the ACCESS-C: Australian Convective-Scale NWP System

Radiance observations are typically affected by biases that come mainly from instrument error (scanning or calibration) and inaccuracies of the radiative transfer model. These biases need to be removed for successful assimilation, so a bias correction scheme is crucial in the Numerical Weather Prediction (NWP) system. Today, most NWP centres, including the Bureau of Meteorology (hereafter, "the Bureau"), correct the biases through variational bias correction (VarBC) schemes, which were originally developed for global models. However, there are difficulties in estimating the biases in a limited-area model (LAM) domain. As a result, the Bureau's regional NWP system, ACCESS-C (Australian Community Climate and Earth System Simulator-City), uses variational bias coefficients obtained directly from its global NWP system ACCESS-G (Global). This study investigates independent radiance bias correction in the data assimilation system for ACCESS-C. We assessed the impact of using independent bias correction for the LAM compared with the operational bias coefficients derived in ACCESS-G between February and April 2020. The results from our experiment show no significant difference between the control and test, suggesting a neutral impact on the forecast. Our findings point out that the VarBC-LAM strategy should be further explored with different settings of predictors and adaptivity for a more extended period and over additional domains.

Impacts of Multi-Source Microwave Satellite Radiance Data Assimilation on the Forecast of Typhoon Ampil

This study investigates the impacts of the joint assimilation of microware temperature sensor, Advanced Microwave Sounding Unit-A (AMSUA), and microware humidity sensors, Microwave Humidity Sounder (MHS) and Microwave Humidity Sounder-2 (MWHS2), on the analyses and forecasts for the tropical cyclone (TC) system. Experiments are conducted using a three-dimensional variation (3DVAR) algorithm in the framework of the weather research and forecasting data assimilation (WRFDA) system for the forecasting of Typhoon Ampil (2018). The results show that the assimilation of MWHS2 radiance data improves the analyses better in terms of TC’s structure and moisture conditions than those of the MHS experiment. To some extent, the experiment with only AMSUA radiance delivers some positive impacts of the precipitation, track, and intensity forecast than the other two experiments do. In addition, the skill of the precipitation forecast is notably enhanced with higher equitable threat score (ETS) by the simultaneous assimilation of the MHS, MWHS2, and AMSUA radiance. Generally, assimilation of radiance from all sources of MHS, MWHS2, and AMSUA could combine the advantages of assimilating each type of sensors rather than individually. The consistent improvement is also confirmed for the TC’s track forecast with reduced error on average, whereas the improvement of intensity forecast is not obvious.

Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts

This study investigated the impact of the assimilation of satellite radiance observations in a three-dimensional variational data assimilation system (3DVAR) that could improve the tracking and intensity forecasts of the Tropical Storm Dianmu in 2021, which occurred over parts of southeast mainland Asia. The weather research and forecasting (WRF) model was used to conduct the assimilation experiments of the storm. Four sets of numerical experiments were performed using the WRF. In the first, the control experiment, only conventional data in Binary Universal Form for the Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) were assimilated. The second experiment (RDA1) was performed with PREPBUFR observations and satellite radiance data from the Advanced Microwave Unit-A (AMSU-A), and the Advanced Technology Microwave Sounder (ATMS). PREPBUFR observations and the High-resolution Infrared Radiation Sounder (HIRS-4) were used in the third experiment (RDA2). The fourth experiment (ALL-OBS) used the assimilation of PREPBUFR observations and all satellite radiance data (AMSU-A, ATMS, and HIRS-4). The community radiative transfer model was used on the forward operator for the satellite radiance assimilation, along with quality control and bias correction procedures, before assimilating the radiance data. To evaluate the impact of the assimilation experiments, a forecast starting on 00 UTC 23 September 2021, was produced for 72 h. The results showed that the ALL-OBS experiment improved the short-term forecast up to ~24 h lead time, as compared to the assimilation considering only PREPBUFR observations. When all observations were assimilated into the model, the storm’s landfall position, intensity, and structure were accurately predicted. In the deterministic forecast, the tracking errors of the ALL-OBS experiment was consistently less than 40 km within 24 h. The case study of Tropical Storm Dianmu exhibited the significant positive impact of all observations in the numerical model, which could improve updates for initial conditions and storm forecasting.

Effect of data assimilation in the Polar WRF with 3DVAR on the prediction of radiation, heat flux, cloud, and near surface atmospheric variables over Svalbard

Although the Polar Weather Research and Forecasting (PWRF) model has been developed for polar environments, simulations of atmospheric states in the polar region still have large uncertainties. Therefore, the effects of data assimilation (DA) to improve forecasts in the polar region were investigated for September 2017 using PWRF and the three-dimensional variational (3DVAR) DA method. The experiments without DA and those with DA assimilating only conventional observations and both conventional observations and satellite radiance data were performed. The forecasts from all experiments both without and with DA underestimated (overestimated) the downward longwave (shortwave) radiation flux due to the underestimation of the amount of Arctic clouds. When satellite radiance data (i.e., the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS)) were assimilated in addition to conventional observations in the PWRF, the distribution and amount of water vapor became closer to observations, which improves cloud liquid water forecasts. Therefore, when both conventional observations and radiance data were assimilated, the 25–30 h forecast errors of the downward longwave and shortwave radiation fluxes and sensible and latent heat fluxes decreased by 12.7%, 8.1%, 3.2%, and 7.8% with the WSM5 scheme and by 17.1%, 4.7%, 2.5%, and 3.1% with the 2-moment Morrison scheme, respectively, compared to those in the experiments without DA. The forecast errors of the 10 m wind and 2 m temperature with DA were smaller than those without DA at most observation stations. Therefore, the uncertainties of the Arctic forecasts in the PWRF decreased when using DA. To further improve Arctic forecasts, the assimilation of various additional satellite data is necessary.

Impacts of initial conditions and model configuration on simulations of polar lows near Svalbard using Polar WRF with 3DVAR

AbstractThe impacts of initial conditions and model configuration on the simulations of polar lows (PLs) near Svalbard, the global “hotspot” for PL activity, were investigated using the Polar Weather Research and Forecasting (Polar WRF or PWRF) model and WRF three‐dimensional data assimilation (3DVAR). The well‐documented PL case presented by Sergeev et al., in 2017 from March 26, 2013 featuring in situ aircraft observations is revisited using PWRF and 3DVAR to identify the most suitable model configuration to simulate PLs. The simulation results using initial conditions from PWRF and 3DVAR cycling runs were compared with those of cold‐start experiments using reanalysis as the initial condition. For cycling runs, the impacts of initial conditions from synoptic observations together with satellite radiances were superior to the assimilation of synoptic data alone. Several additional experiments were undertaken to further refine the PWRF configuration by considering the horizontal resolution, analysis nudging, digital filter initialization, and two boundary‐layer and four microphysics schemes. Based on the chosen configuration of Polar WRF and 3DVAR from the case‐study, the monthly reanalysis mode with 3 hr time window cycling data assimilation and short‐term forecasts using PWRF alone were performed for March 2013 and the PLs that formed over the Nordic Seas were evaluated. Manual identification and verification of PLs were performed using thermal infrared satellite imagery and 10 m scatterometer winds. Overall, nine PLs were identified in addition to the case presented by Sergeev et al., in 2017. Both the case‐study and monthly simulation results show that the high‐resolution initial condition is the most important factor, but a reasonable forecast model configuration can help to improve the simulation performance. Satellite radiance data are very important for producing the high‐resolution initial conditions for Polar WRF to simulate PLs successfully; the best performance was obtained by assimilating both synoptic data and satellite radiances.

Evaluating the Forecast Impact of Assimilating ATOVS Radiance With the Regional System of Multigrid NLS‐4DVar Data Assimilation for Numerical Weather Prediction (SNAP)

AbstractThe regional System of Multigrid Nonlinear Least Squares Four‐dimensional Variational Data Assimilation for Numerical Weather Prediction (SNAP) was recently established based on the multigrid NLS‐4DVar assimilation scheme, Weather Research and Forecasting numerical model, and Gridpoint Statistical Interpolation (GSI)‐based observation quality control and observation operator modules. The analysis variables are model state variables, rather than the control variables adopted in the conventional 4DVar system. The regional SNAP adopts the multigrid NLS‐4DVar, which can correct errors from large to small scales and accelerate iteration solutions, to minimize the cost function and obtain the optimal analysis. Therefore, the regional SNAP has a higher assimilation efficiency and accuracy. In addition, the assimilation performance of the regional SNAP for conventional and radar observations had been evaluated. The main goal of this study is to achieve the direct assimilation of satellite radiation data using the regional SNAP. In this study, 1‐week cycle assimilation experiments assimilating Advanced TIROS Operational Vertical Sounder (ATOVS) data were designed to fully evaluate the performance of regional SNAP compared with the GSI Ensemble Four‐dimensional Variational (4DEnVar) scheme. First, a rainstorm was selected to illustrate the performance of regional SNAP. The cumulative precipitation distribution of SNAP was closer to reality and the higher equitable threat score and lower far score indicate that the regional SNAP improves precipitation forecast. In the 1‐week numerical experiments, for the u/v wind and temperature variables, the regional SNAP outperforms GSI and there was an improvement in GSI for the humidity variable.

Numerical Simulation of Near-Surface Wind during a Severe Wind Event in a Complex Terrain by Multisource Data Assimilation and Dynamic Downscaling

Accurate forecast and simulation of near-surface wind is a great challenge for numerical weather prediction models due to the significant transient and intermittent nature of near-surface wind. Based on the analyses of the impact of assimilating in situ and Advanced Tiros Operational Vertical Sounder (ATOVS) satellite radiance data on the simulation of near-surface wind during a severe wind event, using the new generation mesoscale Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system, the dynamic downscaling of near-surface wind is further investigated by coupling the microscale California Meteorological (CALMET) model with the WRF and its 3DVAR system. Results indicate that assimilating in situ and ATOVS radiance observations strengthens the airflow across the Alataw valley and triggers the downward transport of momentum from the upper atmosphere in the downstream area of the valley in the initial conditions, thus improving near-surface wind simulations. Further investigations indicate that the CALMET model provides more refined microtopographic structures than the WRF model in the vicinity of the wind towers. Although using the CALMET model achieves the best simulation of near-surface wind through dynamic downscaling of the output from the WRF and its 3DVAR assimilation, the simulation improvements of near-surface wind speed are mainly within 1 m s−1. Specifically, the mean improvement proportions of near-surface wind speed are 64.8% for the whole simulation period, 58.7% for the severe wind period, 68.3% for the severe wind decay period, and 75.4% for the weak wind period. The observed near-surface wind directions in the weak wind conditions are better simulated in the coupled model with CALMET downscaling than in the WRF and its 3DVAR system. It is concluded that the simulation improvements of CALMET downscaling are distinct when near-surface winds are weak, and the downscaling effects are mainly manifested in the simulation of near-surface wind directions.

Comparison of Assimilating All-Sky and Clear-Sky Satellite Radiance for Typhoon Chan-Hom and Nangka Forecasts

The impacts of assimilating all-sky satellite radiance from the Advanced Microwave Scanning Radiometer 2 (AMSR2) on typhoon Chan-hom and Nangka are evaluated over traditional clear-sky radiance assimilation. Results show that more AMSR2 radiance data around typhoon core area are assimilated in all-sky experiment than clear-sky, which improves the utilization of satellite radiance data. Community Radiative Transfer Model (CRTM) brightness temperature simulation under all-sky conditions is in better agreement with observations than in the case of clear-sky conditions. In a cycle assimilation experiment, all-sky assimilation reduces typhoon track forecast errors by 14.84%, and intensity errors by approximately 16.89%. Wind, temperature and humidity analysis are clearly improved in all-sky assimilation, as evaluated using the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data. All-sky assimilation better captures the structures of typhoons, with a stronger warm core and tighter circulation around the typhoon eye. This study explores the contributions to the improvements in all-sky assimilation. These improvements are attributed to the enhancements in initial geopotential height, temperature and moisture in the typhoon core areas. Moreover, assimilating cloud- and precipitation-affected radiance data improves hydrometer simulations, which leads to higher hydrometeor concentrations than clear-sky radiance and conventional data assimilation. The results demonstrate that assimilation of all-sky AMSR2 data improves the analysis and forecast of multiple typhoons.

The Framework for Assimilating All-Sky GPM Microwave Imager Brightness Temperature Data in the NASA GEOS Data Assimilation System

Abstract Satellite radiance observations combine global coverage with high temporal and spatial resolution, and bring vital information to NWP analyses especially in areas where conventional data are sparse. However, most satellite observations that are actively assimilated have been limited to clear-sky conditions due to difficulties associated with accounting for non-Gaussian error characteristics, nonlinearity, and the development of appropriate observation operators for cloud- and precipitation-affected satellite radiance data. This article provides an overview of the development of the Gridpoint Statistical Interpolation (GSI) configurations to assimilate all-sky data from microwave imagers such as the GPM Microwave Imager (GMI) in the NASA Goddard Earth Observing System (GEOS). Electromagnetic characteristics associated with their wavelengths allow microwave imager data to be highly sensitive to precipitation. Therefore, all-sky data assimilation efforts described in this study are primarily focused on utilizing these data in precipitating regions. To utilize data in cloudy and precipitating regions, state and analysis variables have been added for ice cloud, liquid cloud, rain, and snow. This required enhancing the observation operator to simulate radiances in heavy precipitation, including frozen precipitation. Background error covariances in both the central analysis and EnKF analysis in the GEOS hybrid 4D-EnVar system have been expanded to include hydrometeors. In addition, the bias correction scheme was enhanced to reduce biases associated with thick clouds and precipitation. The results from single observation experiments demonstrate the capability of assimilating all-sky microwave brightness temperature data in GEOS both when the model forecast produces excessive precipitation and too little precipitation. Additional experiments show that hydrometeors and dynamic variables such as winds and pressure are adjusted in physically consistent ways in response to the assimilation.

The Impact of Satellite Radiance Data Assimilation within a Frequently Updated Regional Forecast System Using a GSI-based Ensemble Kalman Filter

A regional ensemble Kalman filter (EnKF) data assimilation (DA) and forecast system was recently established based on the Gridpoint Statistical Interpolation (GSI) analysis system. The EnKF DA system was tested with continuous three-hourly updated cycles followed by 18-h deterministic forecasts from every three-hourly ensemble mean analysis. Initial tests showed negative to neutral impacts of assimilating satellite radiance data due to the improper bias correction procedure. In this study, two bias correction schemes within the established EnKF DA system are investigated and the impact of assimilating additional polar-orbiting satellite radiance is also investigated. Two group experiments are conducted. The purpose of the first group is to evaluate the bias correction procedure. Two online bias correction methods based on GSI 3DVar and EnKF algorithms are used to assimilate AMSU-A radiance data. Results show that both variational and EnKF-based bias correction procedures effectively reduce the observation and background radiance differences, achieving positive impacts on forecasts. With proper bias correction, we assimilate full radiance observations including AMSU-A, AMSU-B, AIRS, HIRS3/4, and MHS in the second group. The relative percentage improvements (RPIs) for all forecast variables compared to those without radiance data assimilation are mostly positive, with the RPI of upper-air relative humidity being the largest. Additionally, precipitation forecasts on a downscaled 13-km grid from 40-km EnKF analyses are also improved by radiance assimilation for almost all forecast hours.

Fast Fourier-transform calculation of artificial night sky brightness maps

Light pollution poses a growing threat to optical astronomy, in addition to its detrimental impacts on the natural environment, the intangible heritage of humankind related to the contemplation of the starry sky and, potentially, on human health. The computation of maps showing the spatial distribution of several light pollution related functions (e.g. the anthropogenic zenithal night sky brightness, or the average brightness of the celestial hemisphere) is a key tool for light pollution monitoring and control, providing the scientific rationale for the adoption of informed decisions on public lighting and astronomical site preservation. The calculation of such maps from satellite radiance data for wide regions of the planet with sub-kilometric spatial resolution often implies a huge amount of basic pixel operations, requiring in many cases extremely large computation times. In this paper we show that, using adequate geographical projections, a wide set of light pollution map calculations can be reframed in terms of two-dimensional convolutions that can be easily evaluated using conventional fast Fourier-transform (FFT) algorithms, with typical computation times smaller than 10−6 s per output pixel.