Bias Correction Scheme Research Articles

All-Sky Assimilation of GOES-16 Water Vapor Channels In Consideration of Cloud-Dependent Interchannel Observation-Error Correlations

Abstract The Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-16 includes three water vapor channels (8, 9, 10) that are specifically designed to monitor tropospheric water vapor at high, middle, and low levels. This study investigates an effective approach for assimilating the all-sky brightness temperatures (BTs) derived from these three water vapor channels while considering the characteristics of interchannel observation-error correlations (IOECs). The NASA Unified Weather Research and Forecasting (NU-WRF) model, alongside the NCEP Gridpoint Statistical Interpolation (GSI)-based three-dimensional ensemble-variational hybrid data assimilation system, is utilized. First, an optimal bias correction (BC) scheme and data assimilation (DA) configuration, previously developed for all-sky assimilation of GOES-16 channel 8, are confirmed to be effective for channels 9 and 10. Then, the IOECs with respect to the symmetric cloud proxy variable among these three channels are analyzed. It is found that the IOECs display sigmoid function characteristics, being lower and roughly invariant with respect to under clear sky conditions and higher in cloudy conditions. Given the unique properties of IOECs, sensitivity experiments with various configurations for assimilating the all-sky BTs from the three water vapor channels are conducted with Hurricanes Laura (2020) and Ida (2021). The results indicate that a strategy combining the assimilation of all-sky BTs from one of the GOES-16 channels, especially channel 10, with clear-sky BTs from the other water vapor channels yields superior analysis and forecasts in most scenarios, thereby highlighting the importance of appropriately estimating and accounting for cloud-dependent IOECs when assimilating all-sky BTs from the infrared channels in operational DA systems.

Impacts of Offline Nonlinear Bias Correction Schemes Using the Machine Learning Technology on the All‐Sky Assimilation of Cloud‐Affected Infrared Radiances

AbstractBias correction (BC) of the cloud‐affected infrared (IR) radiances is one of the most difficult challenges in the all‐sky data assimilation. This study introduces an offline nonlinear bias correction model based on the machine learning (ML) technology of Random Forest to enhance the impacts of Fengyun‐4A Advanced Geostationary Radiation Imager (AGRI) all‐sky radiance data assimilation. The effects of the developed model were comprehensively evaluated through sensitivity experiments based on the NoBC, BC and modified BC schemes for two super typhoon cases. Among them, the modified BC scheme is designed to extract the features of cloud‐affected systematic biases, which are more prevalent in the all‐sky IR radiance assimilation. Results showed that the modified BC scheme outperforms other schemes in terms of removing the cloud‐impacted systematic bias while retaining the useful meteorological signal. Whereas, those biases were improperly corrected by the original BC scheme when the inputs of a grid point were handled by the ML model site by site without the feature extraction, leading to a non‐Gaussian error distribution. Assimilating those better‐corrected IR radiances in the modified BC experiments would lead to a greater improvement in the analysis of the humidity and cloud ice. Based on the improved initial condition, the positive effects of the modified BC scheme are also evident in the forecasts of atmospheric variables and typhoon systems.

Improving Short-Term, Near-Surface Temperature Forecasts by Integrating Weather Pattern Information into Model Output Statistics

Abstract Dynamical numerical weather prediction has remarkably improved over the last decades. Yet postprocessing techniques are needed to calibrate forecasts which are based on statistical and machine learning techniques. With recent advances in the derivation of year-round, large-scale atmospheric circulations, or weather regimes, the question arises of whether this information can be valuable within forecast postprocessing methods. This paper investigates this by proposing a bias correction scheme to integrate the atmospheric circulation state derived from empirical orthogonal functions, referred to as weather patterns, for deterministic short-term, near-surface temperature forecasts based on least absolute shrinkage and selection operator (LASSO) regression. We propose a computational study which first evaluates different weather pattern definitions (spatial domain) to improve temperature forecasts in Europe. As a bias could be associated with the weather pattern at the model initialization time or at the realization time of the forecast, both variants are tested in this study. We show that forecasted weather patterns with the identical spatial domain as the forecast show best skill reaching mean-square-error skill improvements of up to 3% (day ahead) or 1% (week ahead). Only considering land surface improvements in Europe, improvements of 4%–6% for day-ahead forecasts and 1%–5% for week-ahead forecasts are observable. We believe that this study not only introduces a simple yet effective tool to reduce bias in temperature forecasts but also contributes to the active discussion of how valuable weather patterns are and how to use them within forecast calibration techniques.

Combined assimilation of NOAA surface and MIPAS satellite observations to constrain the global budget of carbonyl sulfide

Abstract. Carbonyl sulfide (COS), a trace gas in our atmosphere that leads to the formation of aerosols in the stratosphere, is largely taken up by terrestrial ecosystems. Quantifying the biosphere uptake of COS could provide a useful quantity to estimate gross primary productivity (GPP). Some COS sources and sinks still contain large uncertainties, and several top-down estimates of the COS budget point to an underestimation of sources, especially in the tropics. We extended the inverse model TM5-4DVAR to assimilate Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) satellite data, in addition to National Oceanic and Atmospheric Administration (NOAA) surface data as used in a previous study. To resolve possible discrepancies among the two observational data sets, a bias correction scheme is necessary and implemented. A set of inversions is presented that explores the influence of the different measurement streams and the settings of the prior fluxes. To evaluate the performance of the inverse system, the HIAPER Pole-to-Pole Observations (HIPPO) aircraft observations and NOAA airborne profiles are used. All inversions reduce the COS biosphere uptake from a prior value of 1053 GgS a−1 to much smaller values, depending on the inversion settings. These large adjustments of the biosphere uptake often turn parts of Amazonia into a COS source. Only inversions that exclusively use MIPAS observations, or strongly reduce the prior errors on the biosphere flux, maintain the Amazon as a COS sink. Inclusion of MIPAS data in the inversion leads to a better separation of land and ocean fluxes. Over the Amazon, these inversions reduce the biosphere uptake from roughly 300 to 100 GgS a−1, indicating a strongly overestimated prior uptake in this region. Although a recent study also reported reduced COS uptake over the Amazon, we emphasise that a careful construction of prior fluxes and their associated errors remains important. For instance, an inversion that gives large freedom to adjust the anthropogenic and ocean fluxes of CS2, an important COS precursor, also closes the budget satisfactorily with much smaller adjustments to the biosphere. We achieved better characterisation of biosphere prior and uncertainty, better characterisation of combined ocean and land fluxes, and better constraint of both by combining surface and satellite observations. We recommend more COS observations to characterise biosphere and ocean fluxes, especially over the data-poor tropics.

Estimating Kappa within a Low-Seismicity Region in Northern New Mexico Using Data Recorded by the Los Alamos Seismic Network

ABSTRACT We quantify the total attenuation, κ, and the attenuation component due to near-surface site effects, κ0, in a region in northern New Mexico using data recorded by the Los Alamos Seismic Network. The area is characterized by low seismicity, where most of the well-recorded earthquakes have magnitudes between 1 and 2. This magnitude range poses a challenge for commonly used kappa methods because the high-frequency attenuation cannot be confidently isolated from the bandwidth in which the corner frequency roll-off occurs. We determine through synthetic experiments that estimates of κ within this range have quantifiable biases that depend on source (corner frequency), site (κ magnitude), and data quality characteristics (fitting bandwidth), which can be used to correct estimated κ from three commonly used kappa methods. Using 412 recorded earthquakes, we show that a bias correction results in κ distributions and κ0 estimates that are more consistent between the three methods, suggesting that the bias correction results in κ values with higher fidelity. Using the bias-corrected κ, we find κ0 between 0.038 and 0.049 s within the Valles Caldera and between 0.026 and 0.066 s on Los Alamos National Laboratory property, values near those commonly used in the western United States. We find that a main limitation in the quality of κ0 is the small number of usable waveforms at some stations, which will to improve as more earthquakes are recorded. This contrasts with other aspects, such as fitting bandwidth and source and path variability, which are unlikely to change in the future and will ultimately be the limiting factor in κ0 resolution. Overall, our results suggest that the bias-correction scheme presented here could potentially be used in other regions where small-magnitude earthquakes are prevalent. However, future work should look to verify that bias-corrected κ estimates show consistency with those retrieved from higher magnitude earthquakes.

Disequilibrium oxygen isotope distribution among aqueously altered minerals in Ryugu asteroid returned samples

AbstractOxygen 3‐isotope ratios of magnetite and carbonates in aqueously altered carbonaceous chondrites provide important clues to understanding the evolution of the fluid in the asteroidal parent bodies. We conducted oxygen 3‐isotope analyses of magnetite, dolomite, and breunnerite in two sections of asteroid Ryugu returned samples, A0058 and C0002, using a secondary ion mass spectrometer (SIMS). Magnetite was analyzed by using a lower primary ion energy that reduced instrumental biases due to the crystal orientation effect. We found two groups of magnetite data identified from the SIMS pit morphologies: (1) higher δ18O (from 3‰ to 7‰) and ∆17O (~2‰) with porous SIMS pits mostly from spherulitic magnetite, and (2) lower δ18O (~ −3‰) and variable ∆17O (0‰–2‰) mostly from euhedral magnetite. Dolomite and breunnerite analyses were conducted using multi‐collection Faraday cup detectors with precisions ≤0.3‰. The instrumental bias correction was applied based on carbonate compositions in two ways, using Fe and (Fe + Mn) contents, respectively, because Ryugu dolomite contains higher amounts of Mn than the terrestrial standard. Results of dolomite and breunnerite analyses show a narrow range of ∆17O; 0.0‰–0.3‰ for dolomite in A0058 and 0.2‰–0.8‰ for dolomite and breunnerite in C0002. The majority of breunnerite, including large ≥100 μm grains, show systematically lower δ18O (~21‰) than dolomite (25‰–30‰ and 23‰–27‰ depending on the instrumental bias corrections). The equilibrium temperatures between magnetite and dolomite from the coarse‐grained lithology in A0058 are calculated to be 51 ± 11°C and 78 ± 14°C, depending on the instrumental bias correction scheme for dolomite; a reliable temperature estimate would require a Mn‐bearing dolomite standard to evaluate the instrumental bias corrections, which is not currently available. These results indicate that the oxygen isotope ratios of aqueous fluids in the Ryugu parent asteroid were isotopically heterogeneous, either spatially, or temporary. Initial water ice accreted to the Ryugu parent body might have ∆17O > 2‰ that was melted and interacted with anhydrous solids with the initial ∆17O < 0‰. In the early stage of aqueous alteration, spherulitic magnetite and calcite formed from aqueous fluid with ∆17O ~ 2‰ that was produced by isotope exchange between water (∆17O > 2‰) and anhydrous solids (∆17O < 0‰). Dolomite and breunnerite, along with some magnetite, formed at the later stage of aqueous alteration under higher water‐to‐rock ratios where the oxygen isotope ratios were nearly at equilibrium between fluid and solid phases. Including literature data, δ18O of carbonates decreased in the order calcite, dolomite, and breunnerite, suggesting that the temperature of alteration might have increased with the degree of aqueous alteration.

A Machine Learning-Based Bias Correction Scheme for the All-Sky Assimilation of AGRI Infrared Radiances in a Regional OSSE Framework

A Machine Learning-Based Bias Correction Scheme for the All-Sky Assimilation of AGRI Infrared Radiances in a Regional OSSE Framework

Impact of a New Bias Correction Predictor for FY‐4A AGRI All‐Sky Data Assimilation on Typhoon Forecast

AbstractThe all‐sky assimilation module for Advanced Geostationary Radiation Imager (AGRI) onboard the Chinese new generation of geostationary meteorological satellites Fengyun‐4A (FY‐4A) is constructed for the first time in Weather Research and Forecasting Model Data Assimilation (WRFDA) model with Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV). Based on the characteristics of bias distribution, the cloud effect average is selected as a new bias correction (BC) predictor to remove cloud‐related biases in all‐sky conditions. The impact of the modified BC scheme and AGRI all‐sky assimilation on typhoon Lekima (2019) forecast is evaluated through a set of cycle assimilation experiments with WRFDA 3DVAR component. The result shows that, compared with other BC schemes, the modified BC scheme can effectively reduce the mean standard deviation, root mean square error, and absolute mean of observation departure in all‐sky conditions. Meanwhile, the AGRI all‐sky assimilation with the modified BC scheme obtains significant improvements in track prediction. Substantial error reductions in model variables, such as wind, temperature, and humidity, are also produced.

Impact of assimilating Aeolus observations in the global model ICON: A global statistical overview

AbstractGlobal wind profiles provided by the satellite mission Aeolus are an important recent supplement to the Global Observing System. This study investigates the impact of Aeolus horizontal line‐of‐sight wind observations in the operational global assimilation and forecasting system of Deutscher Wetterdienst that is based on the Icosahedral Nonhydrostatic (ICON) model. For this purpose, an observing system experiment was conducted and evaluated for a 3‐month period from July 2020 to October 2020. The Aeolus Rayleigh clear and Mie cloudy data quality and consistency were derived from observation minus background statistics. To correct for an altitude‐dependent bias, a model‐based bias correction scheme has been implemented. Comparisons of the systematic changes in the analysis and the respective forecasts provide an overview of the overall impact of the Aeolus horizontal line‐of‐sight wind assimilation in ICON. Increased influence of Aeolus wind profiles is found in jet regimes (e.g., amplification of the zonal wind component), around large‐scale circulation systems, and convectively active areas in the Tropics. The reduction in forecast error is largest in the tropical upper troposphere and stratosphere, as well as in the mid and upper troposphere of the Southern Hemisphere. The Northern Hemisphere shows a somewhat smaller but still beneficial impact of Aeolus observations. The verification with other conventional observations shows a mean relative reduction in short‐range forecast error between 0.1% and 0.6% in the Northern Hemisphere and up to 1.6% in the Tropics and the Southern Hemisphere. When verifying against the European Centre for Medium‐Range Weather Forecast Reanalysis v5, forecast errors of zonal wind, temperature, and geopotential up to 5 days lead time are reduced by 2–4% on global average and up to 5–8% around the tropical tropopause.

Debiased inference on heterogeneous quantile treatment effects with regression rank scores

Abstract Understanding treatment effect heterogeneity is vital to many scientific fields because the same treatment may affect different individuals differently. Quantile regression provides a natural framework for modelling such heterogeneity. We propose a new method for inference on heterogeneous quantile treatment effects (HQTE) in the presence of high-dimensional covariates. Our estimator combines an ℓ1-penalised regression adjustment with a quantile-specific bias correction scheme based on rank scores. We study the theoretical properties of this estimator, including weak convergence and semi-parametric efficiency of the estimated HQTE process. We illustrate the finite-sample performance of our approach through simulations and an empirical example, dealing with the differential effect of statin usage for lowering low-density lipoprotein cholesterol levels for the Alzheimer’s disease patients who participated in the UK Biobank study.

Improving sea surface temperature in a regional ocean model through refined sea surface temperature assimilation

Abstract. Infrared (IR) and passive microwave (PMW) satellite sea surface temperature (SST) retrievals are valuable to assimilate into high-resolution regional ocean forecast models. Still, there are issues related to these SSTs that need to be addressed to achieve improved ocean forecasts. Firstly, satellite SST products tend to be biased. Assimilating SSTs from different providers can thus cause the ocean model to receive inconsistent information. Secondly, while PMW SSTs are valuable for constraining models during cloudy conditions, the spatial resolution of these retrievals is rather coarse. Assimilating PMW SSTs into high-resolution ocean models will spatially smooth the modeled SST and consequently remove finer SST structures. In this study, we implement a bias correction scheme that corrects satellite SSTs before assimilation. We also introduce a special observation operator, called the supermod operator, into the Regional Ocean Modeling System (ROMS) four-dimensional variational data assimilation algorithm. This supermod operator handles the resolution mismatch between the coarse observations and the finer model. We test the bias correction scheme and the supermod operator using a setup of ROMS covering the shelf seas and shelf break off Norway. The results show that the validation statistics in the modeled SST improve if we apply the bias correction scheme. We also find improvements in the validation statistics when we assimilate PMW SSTs in conjunction with the IR SSTs. However, our supermod operator must be activated to avoid smoothing the modeled SST structures on spatial scales smaller than twice the PMW SST footprint. Both the bias correction scheme and the supermod operator are easy to apply, and the supermod operator can easily be adapted for other observation variables.

Nonlinear Bias Correction of the FY-4A AGRI Infrared Radiance Data Based on the Random Forest

Bias correction is a key prerequisite for radiance data assimilation. Directly assimilating the radiance observations generally involves large systematic biases affecting the numerical prediction accuracy. In this study, a nonlinear bias correction scheme with Random Forest (RF) technology is firstly proposed based on the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) channels 9–10 observations in the Weather Research and Forecasting Data Assimilation (WRFDA) system. Two different settings of the predictors are additionally designed and evaluated based on the performance of the RF model. It seems that an apparent scene temperature-dependent bias could be effectively resolved by the RF scheme when applying the RF method with newly added predictors. Results suggest that the proposed nonlinear scheme of RF performs better than the linear scheme does in terms of reducing the systematic biases. A more idealized error distribution of observation minus background (OMB) is found in the RF-based experiments that measure the nonlinear relationship between the OMB biases and the predictors when using the Gaussian distribution as the reference. Furthermore, the RF scheme shows a consistent improvement in bias correction with the potential to ameliorate the atmospheric variables of analyses.

Systematic diurnal bias of the CMA-MESO model in southern China: Characteristics and correction

Model error is an important source of numerical weather prediction (NWP) errors. Among model errors, the systematic diurnal bias plays an important role in high-resolution numerical weather prediction models. The main purpose of this study is to explore the characteristics of the systematic diurnal bias of a high-resolution NWP model in southern China and reduce the diurnal bias to improve the forecast results, hence providing a better background field for data assimilation. Based on the China Meteorological Administration Meso-scale (CMA-MESO) high-resolution NWP model, a 15-day sequential numerical weather prediction experiment was performed in southern China, and the forecast results were analyzed. A sequential bias correction scheme (SBCS) based on analysis increments was designed to reduce the systematic diurnal bias of the CMA-MESO model, and 15-day sequential comparative experiments were carried out. The analysis results showed that the CMA-MESO model has apparent systematic diurnal biases, and the characteristics differ among variables. A large diurnal bias was mainly found in the lower model layers, and it was concentrated in areas with a complex underlying surface for the horizontal distribution, such as the Qinghai-Tibet Plateau and South China Coast. The results based on the 15-day sequential experiment showed that the sequential bias correction scheme partly reduced the systematic diurnal biases of the CMA-MESO model. The mean biases of meridional wind, zonal wind, potential temperature, and water vapor mixing ratio were reduced by 45%, 35%, 20%, and 10%, respectively, and the root mean square errors (RMSEs) were reduced by approximately 5%. This study revealed the characteristics of the systematic diurnal bias of CMA-MESO model in southern China, which may be caused by the diurnal variation in the thermal and dynamic exchange on underlying surfaces. The effectiveness of the sequential bias correction scheme was also verified, and the results had good prospects for providing more reference information for high-resolution numerical prediction models and data assimilation.

Assimilation of GNSS Zenith Total Delay in NAVGEM

AbstractA new data source from ground‐based stations that track global navigation satellite system (GNSS) transmitters has been implemented in the Navy Global Environmental Model (NAVGEM) with the NRL Atmospheric Variational Data Assimilation System‐Accelerated Representer. The observable is the ground‐based zenith total delay (ZTD) at each ground station. This ground‐based GNSS ZTD represents the tropospheric delay of the propagation of GNSS radio L‐band signal between the transmitters and the ground stations after correcting for ionospheric effects. Here, we present the implementation procedure, quality control, single observation test, bias correction scheme, and data impact assessment. One of the critical elements in the quality control scheme is to correct for the difference between the observing ground station elevation and the numerical model terrain. A single observation test shows comparable impact from this new data source to a single radiosonde observation at a single level. The ZTD biases estimated from a 6‐month experiment run are generally small, but have a dependency on the processing center and are computed separately for each station. The forecast sensitivity to observation impact diagnosis from a 3‐month experiment demonstrates that a single ZTD observation shows similar impact as the average impact from a single tangent point of a GNSS Radio Occultation profile. The assimilation of ZTD observations also significantly improves the forecast skill by 0.25%–0.75% for wind, temperature, and precipitable water and by 1%–2% for geopotential height in the Southern Hemisphere beyond 3 days.

A Simple Bias Correction Scheme in Ocean Data Assimilation

The mode bias is present and time-dependent due to imperfect configurations. Data assimilation is the process by which observations are used to correct the model forecast, and is affected by the bias. How to reduce the bias is an important issue. This paper investigates the roles of a simple bias correction scheme in ocean data assimilation. In this scheme, the misfits between modeled and monthly temperature and salinity with interannual variability from the Met Office Hadley Centre subsurface temperature and salinity data set (EN4.2.2) are used for the innovations in assimilation via the Ensemble Optimal Interpolation method. Two assimilation experiments are implemented to evaluate the impacts of bias correction. The first experiment is a data assimilation system without bias correction. In the second experiment, the bias correction is applied in assimilation. For comparison, the nature run with no assimilation and no bias correction is also conducted. When the bias correction is not applied, the assimilation alone leads to a rising trend in the heat and salt content that is not found in the observations. It is a spurious temporal variability due to the effect of the bias on the data assimilation. Meanwhile, the assimilation experiment without bias correction also produces significant negative impacts on the subsurface salinity. The experiment with bias correction performs best with notable improvements over the results of the other two experiments.

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.

A Bias Correction Scheme with the Symmetric Cloud Proxy Variable and Its Influence on Assimilating All-Sky GOES-16 Brightness Temperatures

Abstract All-sky assimilation of brightness temperatures (BTs) from GOES-16 infrared water vapor channels is challenging, primarily because these channels are sensitive to cloud ice that causes large nonlinear errors in the forecast and forward models. Thus, bias correction (BC) for all-sky assimilation of GOES-16 BTs is vital. This study examines the impacts of different BC schemes, especially for a scheme with a quartic polynomial of cloud predictors (the ASRBC4 scheme), on the analysis and WRF Model forecasts of tropical cyclones when assimilating the all-sky GOES-16 channel-8 BTs using the NCEP GSI-based 3D ensemble–variational hybrid data assimilation (DA) system with variational BC (VarBC). Long-term statistics are performed during the NASA Convective Processes Experiment field campaign (2017). Results demonstrate that the ASRBC4 scheme effectively reduces the average of all-sky scaled observation-minus-backgrounds (OmBs) in a cloudy sky and alleviates their nonlinear conditional biases with respect to the symmetric cloud proxy variable, in contrast to the BC schemes without the cloud predictor or with a first-order cloud predictor. In addition, adopting the ASRBC4 scheme in DA decreases the positive temperature increments at 200 hPa and the accompanying midlevel cyclonic wind increments in the analysis of Tropical Storm (TS) Cindy (2017). Applying the ASRBC4 scheme also leads to better storm-track predictions for TS Cindy (2017) and Hurricane Laura (2022), compared to experiments with other BC schemes. Overall, this study highlights the importance of reducing nonlinear biases of OmBs in a cloudy sky for successful all-sky assimilation of BTs from GOES-16 infrared water vapor channels.

Enhancement of Satellite Precipitation Estimations with Bias Correction and Data-Merging Schemes for Flood Forecasting

Enhancement of Satellite Precipitation Estimations with Bias Correction and Data-Merging Schemes for Flood Forecasting

Bias correction of GPM IMERG Early Run daily precipitation product using near real-time CPC global measurements

This study focused on improving the performance of the near real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Early Run (IMERG-E) product based on a newly developed bias-correction scheme, LSCDF. The LSCDF was established by integrating the mean-based Linear Scaling (LS) and quantile-mapping Cumulative Distribution Function (CDF) matching approaches. The daily updating gauge-based precipitation data from the Climate Prediction Center (CPC) unified analysis were used as the benchmark for bias correction. The IMERG-E bias-corrected precipitation data were evaluated against the daily ground measurements from 807 meteorological stations across mainland China and compared with the raw IMERG-E and IMERG Final Run (IMERG-F; research-level with gauge calibration) retrievals. Evaluation results for the period 2015 to 2017 showed that the LSCDF method effectively improves near real-time IMERG-E precipitation estimates at the daily scale. The bias-corrected IMERG-E precipitation estimates were in significantly better agreement with ground measurements than the original IMERG-E at the point-daily resolutions, with the correlation coefficient increasing from 0.66 to 0.77, relative bias decreasing from 11.1% to −3.1%, and root-mean-square error dropping from 6 mm/day to 4.39 mm/day. In addition, the bias-corrected IMERG-E was apparently superior to IMERG-E in detecting precipitation events at various intensity levels including small, light, moderate, and heavy precipitation. Although IMERG-E tended to significantly underestimate or overestimate precipitation during three typical typhoons, the bias-corrected IMERG-E can match the rainfall spatial distributions well, demonstrating a considerable capability in capturing the extreme precipitation. Generally, the bias-corrected IMERG-E featured a substantial improvement over the original IMERG-E, and it even exhibited a more satisfactory overall performance than the research-level gauge-calibrated IMERG-F product. Our study demonstrates that the bias-correction method LSCDF can improve the accuracy of satellite precipitation products to benefit the near real-time application of satellite products for natural hazards.

Effects of Direct Assimilation of FY-4A AGRI Water Vapor Channels on the Meiyu Heavy-Rainfall Quantitative Precipitation Forecasts

In this study, the regional Weather Research and Forecasting model (WRF)-based quantitative precipitation forecasts (QPFs) are conducted for an extreme Meiyu rainfall event over East Asia in 2020. The data of water vapor channels 9 and 10 from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Fengyun-4A (FY-4A) satellite are assimilated through the Gridpoint Statistical Interpolation (GSI) system. It shows that a reasonable amount of assimilated AGRI data can produce reasonable water vapor increments, compared to the too sparse or dense assimilated AGRI observations. In addition, the critical success indexes (CSIs) of the precipitation forecasts within 72 h are obviously improved. The enhanced variational bias correction (VarBC) scheme is applied to remove the air-mass and scan-angle biases, and the mean observation-minus-background (O − B) values before and after the VarBC of channel 9 are −1.185 and 0.02 K, respectively, and those of channel 10 are −0.559 and −0.01 K, respectively. Assimilating the upper-level channel 9 data of AGRI (EXP_WV9) lead to a neutral-to-positive effect on QPFs, compared to the control run (CTL), which is based on the assimilation of Advanced Microwave Sounding Unit-A (AMSU-A) data. In particular, the CSIs from 42 to 72 h are significantly improved. However, the assimilation of the AGRI channel 10 (EXP_WV10) shows a neutral-to-negative effect on QPFs in this study, probably due to the complicated surface situations. This study confirms the feasibility of assimilating the water vapor channel data of FY4A AGRI in the GSI system and highlights the importance of assimilating AGRI channel 9 data to improve the QPFs of the Meiyu rainfall event.