Direct Assimilation Research Articles

The Direct Assimilation of Radar Reflectivity Data with a Two-Moment Microphysics Scheme for a Landfalling Typhoon in an OSSE Framework

Despite the well-known importance of radar data assimilation, there are limited studies on landfalling typhoons in terms of directly assimilating radar reflectivity data, especially using a reflectivity operator based on double-moment microphysics. In this study, radar reflectivity data assimilation experiments are conducted with an ensemble Kalman filter (EnKF), using simulated observations in an Observing System Simulation Experiment (OSSE) framework for the landfalling typhoon In-Fa. With an OSSE, it is convenient to analyze the impact of assimilation of radar reflectivity on analysis and forecast for various state variables, especially for hydrometeors. Our results show that the direct assimilation of radar reflectivity with EnKF does not introduce non-physical hydrometeors and is able to adjust well, not only to hydrometers, but also to some large-scale variables which are not directly related to reflectivity, especially in terms of temperature and vertical velocity. Though the most notable reduction in the Root Mean Square Errors (RMSEs) is observed through mixing the ratio of rainwater and snow, the analysis of other variables is also significantly improved with the accumulation of assimilation cycles. The correlation analysis reveals the strongest correlation between radar reflectivity data and hydrometeor-related variables as well as the correlation with certain large-scale variables, indicating that these cross-variables are updated well through the reliable multivariate ensemble covariance in the EnKF. As a result, an obvious improvement in typhoon intensity and precipitation forecast is obtained in the data assimilation experiment. The impact of assimilation on radar reflectivity can last for up to 15–16 h.

Transcriptomic data reveals an auxiliary detoxification mechanism that alleviates formaldehyde stress in Methylobacterium sp. XJLW

Methylobacterium sp. XJLW converts formaldehyde into methanol and formic acid via a Cannizzaro reaction in response to environmental formaldehyde stress. Methanol is further assimilated without formaldehyde or formic acid formation, whereas formic acid accumulates without undergoing further metabolism. Synthetic biology-based biotransformation of methanol to generate additional products can potentially achieve carbon neutrality. However, practical applications are hampered by limitations such as formaldehyde tolerance. In this study, we aimed to explore the specific mechanism of strain XJLW in response to formaldehyde stress. Thus, a transcriptomic analysis of XJLW under formaldehyde treatment was performed, revealing changes in the expression of specific genes related to one-carbon metabolism. Central metabolic genes were downregulated, whereas metabolic bypass genes were upregulated to maintain methanol assimilation in XJLW’s response to formaldehyde treatment. In total, 100 genes potentially related to methyl transfer were identified. The function of only one gene, RS27765, was similar to that of glyA, which encodes a methyltransferase involved in one-carbon metabolism. The double-mutant strain, lacking RS27765 and glyA, lost its ability to grow in methanol, whereas the single-mutant strain, lacking only one of these genes, still grew in methanol. Co-expression of RS27765 and RS31205 (YscQ/HrcQ type III secretion apparatus protein) enabled Escherichia coli BL21 (DE3) to effectively degrade methanol. Using protein sequence analysis and molecular docking, we proposed a model wherein RS27765 is necessary for cell growth by using methanol generated via formaldehyde cannizzaro reaction. This process enables direct assimilation of methanol without producing formaldehyde and formic acid as intermediate metabolites. The RS27765 gene cluster, in conjunction with metabolic bypass genes, constitutes a novel auxiliary pathway facilitating formaldehyde stress tolerance in the strain.

Assimilation of Satellite-Derived Reservoir Storage Data to Improve Global Hydrodynamic Modeling

In this study, we explore the potential of assimilating satellite-derived reservoir storage data into the global-scale hydrodynamic model CaMa-Flood, focusing on the Yangtze River basin. We evaluated three data assimilation (DA) methods: direct assimilation (DIR), anomaly based assimilation (ANO), and normalized assimilation (NOM). Our results show that the DIR method achieved the most significant improvements in reservoir storage and downstream discharge simulations. DIR reduced the average relative root mean square error (rRMSE) of reservoir storage estimates by 80.5%, and increased discharge correlation (ΔCC) by 78.6% in the 14 validated discharge stations. ANO, while effective in certain cases, led to mixed results, with 56.4% of the 39 assimilated dams showing improved storage estimates and a modest 7.8% reduction in average RMSE. NOM had minimal impact, with negligible changes in RMSE or discharge correlation (ΔCC). The direct assimilation method (DIR) consistently outperformed the others, improving both reservoir storage and downstream discharge estimates. However, the magnitude of improvement varied across locations, highlighting the need for the further refinement of DA techniques and input data, especially for regions with complex reservoir operations. Our findings enhance reservoir representation in global hydrodynamic models and improve the predictability of river dynamics and water resource management.

ORIGIN AND AGE OF CARBON IN THE CELLULOSE OF MID-LATITUDE TREE RINGS

ABSTRACT Cellulose of tree rings is often assumed to be predominantly formed by direct assimilation of CO2 by photosynthesis and consequently can be used to reconstruct past atmospheric 14C concentrations at annual resolution. Yet little is known about the extent and the age of stored carbon from previous years used in addition to the direct assimilation in tree rings. Here, we studied 14C in earlywood and latewood cellulose of four different species (oak, pine, larch and spruce), which are commonly used for radiocarbon calibration and dating. These trees were still growing during the radiocarbon bomb peak period (1958–1972). We compared cellulose 14C measured in tree-ring subdivisions with the atmospheric 14C corresponding to the time of ring formation. We observed that cellulose 14C carried up to about 50% of the atmospheric 14C signal from the previous 1–2 years only in the earlywood of oak, whereas in conifers it was up to 20% in the earlywood and in the case of spruce also in the latewood. The bias in using the full ring of trees growing in a temperate oceanic climate to estimate atmospheric 14C concentration might be minimal considering that earlywood has a low mass contribution and that the variability in atmospheric 14C over a few years is usually less than 3‰.

Assimilation of Transformed Retrievals from IASI radiances versus direct assimilation of IASI radiances at the Met Office

AbstractSatellite radiances make up the vast majority of observations that are assimilated routinely at the Met Office and at other major numerical weather prediction (NWP) centres. Improvements in the use of these data have led to significant improvements in NWP forecast skill over the last 30 years. Since the advent of hyperspectral sensors, capable of measuring infrared radiance emerging from the atmosphere over thousands of channels, data providers and NWP centres have been facing the challenges of distributing, monitoring, and assimilating a considerable wealth of data. Forthcoming hyperspectral sensors such as the Infrared Atmospheric Sounding Interferometer New Generation (IASI‐NG) on MetOp Second Generation (Metop‐SG) and the Meteosat Third Generation Infrared Sounder (MTG‐IRS) will increase the amount of data to be considered for assimilation even further. It has been shown that Transformed Retrievals (TRs) generated from a spectrum of radiances can represent most of the information content of the radiances in a significantly reduced set of TR components. In this article we show for the first time, to our knowledge, that by assimilating TRs in an operational data assimilation system it is possible to achieve forecast skill gains similar to those when the same radiances used to generate the TRs are assimilated directly. These results pave the way to exploiting an order‐of‐magnitude larger number of channels from hyperspectral sounders for operational NWP.

Direct assimilation of radar reflectivity using an ensemble 3DEnVar approach to improve analysis and forecasting of tornadic supercells over eastern China

AbstractAn ensemble three‐dimensional ensemble‐variational (3DEnVar) data assimilation (En3DA) approach that directly assimilates radar reflectivity was developed based on the Weather Research and Forecasting model data assimilation system. This system adopts radar reflectivity as the control variable to avoid the need for a tangent linear and adjoint of the observation operator. Flow‐dependent covariance was introduced via ensemble forecasts updated by a group of 3DEnVar. The performance of the En3DA system was examined for two selected cases of high‐impact severe tornadic supercells over China. Results for both cases indicated that the structure of the storms in terms of intensity, coverage, and associated low‐level mesocyclones were analysed more accurately when using the En3DA approach than when adopting the 3DVar method. Hydrometeor analysis showed that En3DA provided a more physically reasonable increment of hydrometeors compared to 3DVar, especially for the graupel mixing ratio. Furthermore, the En3DA forecast was better than the 3DVar forecast throughout the forecast period for both studied cases. En3DA produced smaller errors in terms of intensity and location for supercell forecasts with respect to reflectivity and reflectivity swaths. Furthermore, the quantitative forecast skill of radar reflectivity was improved using En3DA. Errors in the wind, temperature, and water vapor forecast fields produced by En3DA were also reduced compared to those of 3DVar. Diagnostics revealed that En3DA predicted an enhanced low‐level cold pool and stronger outflows in the forward‐flank downdraft and the rear‐flank downdraft regions, which are important for tornadogenesis.

Biological nitrogen removal characteristics and mechanisms of a novel salt-tolerant strain Vibrio sp. LV-Q1 and its application capacity for high-salinity nitrogen-containing wastewater treatment

High concentration of salts in wastewater can adversely affects the metabolic activities and growth of microorganisms, resulting in reduced efficiency of biological nitrogen removal. Screening high-efficiency heterotrophic nitrification-aerobic denitrification (HN-AD) bacteria with salt tolerance is important for the wastewater treatment. In this study, a novel salt-tolerant strain LV-Q1 capable of HN-AD was isolated and identified as Vibrio sp. This strain demonstrates a salinity tolerance range of 0–8 %. Notably, strain LV-Q1 exhibited exceptional nitrogen removal efficiencies (at initial concentrations of 100 mg/L), achieving 97.97 % for NH4+-N, 100 % for NO3--N, and approximately 100 % for mixed nitrogen (NH4+-N and NO3--N) at a salinity of 5 %. Optimal ammonium removal parameters for LV-Q1 were established. The nitrogen removal pathway of LV-Q1 primarily involves direct assimilation of ammonium and its conversion into N2 through HN-AD. Analysis of the salt tolerance mechanism revealed LV-Q1's ability to equilibrate osmotic pressure through the accumulation of amino acids and betaine, production of antioxidative enzymes or extracellular protein, and utilization of its quorum sensing (QS) properties to withstand high-salinity environments. The strain LV-Q1 exhibited strong resistance to salinity shock in bioaugmentation within a moving bed biofilm reactor (MBBR), markedly enhancing nitrogen and carbon removal performance with an improved removal efficiency of 26.74 % and 10.15 % averagely at the end of the reaction at a salinity of 5 %. Moreover, Vibrio sp. LV-Q1 successfully proliferated and dominated, and its relative abundance gradually increased to 59.81 %. This study contributes new insights and resources for the treatment of nitrogen in saline effluents.

Direct assimilation of simulated radar reflectivity for typhoon In-fa using EnKF: Issue with state variables updating

Using a convective scale WRF-GSI system and a reflectivity observation operator based on the double-moment microphysics (Thompson) scheme, simulated radar reflectivity data are produced and then directly assimilated with EnKF through Observing System Simulation Experiments (OSSEs) for the case of typhoon In-Fa (2021). We examined the ability of the EnKF to simultaneously estimate state variables and conducted sensitivity tests to evaluate the impact of updating different state variables. The results show that updating a full set of analysis variables can help obtain highly precise initial fields in the model and improve typhoon forecast skills. Excluding the horizontal wind update will affect the adjustment of the temperature field and the sea level pressure field during the cyclic assimilation process. Updating the variables directly related to the reflectivity operator alone could adjust hydrometers well, but the positive impact arising from the assimilation quickly vanishes during the forecast. In addition, this study also includes a quantitative RMSE analysis for each variable during the assimilation cycle and compares the effect of each schemes on different variables.

Improving the representation of the atmospheric boundary layer by direct assimilation of ground‐based microwave radiometer observations

AbstractIn a joint effort, MeteoSwiss and Deutscher Wetterdienst (DWD) address the need for improving the initial state of the atmospheric boundary layer (ABL) by exploiting ground‐based profiling observations that aim to fill the existing observational gap in the ABL. We implemented brightness‐temperature observations from ground‐based microwave radiometers (MWRs) in our data assimilation systems using a local ensemble transform Kalman filter (LETKF) with Radiative Transfer for TIROS Operational Vertical Sounder, ground‐based (RTTOV‐gb) as a forward operator. We were able to obtain a positive impact on the brightness temperature first guess and analysis, as well as a slight impact on the ABL humidity, using two MWRs at MeteoSwiss. These results led to a subsequent operational implementation of the observing system at MeteoSwiss. Furthermore, we performed an extensive set of assimilation experiments at DWD to investigate further various aspects such as the vertical localisation of selected single channels. We obtained a positive impact on the 6‐hr forecast of ABL temperature and humidity by assimilating two channels employing a dynamical localisation based on the sensitivity functions of RTTOV‐gb but also with a static localisation in a single‐channel setup. Our experiments indicate the importance of vertical localisation when using more than one channel, although reliable improvements are challenging to obtain without a larger number of observations for both assimilation and verification.

Evaluating Direct Assimilation of Satellite-Based Potential Evapotranspiration into SWAT for Improving Hydrological Modeling

Evaluating Direct Assimilation of Satellite-Based Potential Evapotranspiration into SWAT for Improving Hydrological Modeling

Direct assimilation of AVHRR satellite radiance measurements in a reanalysis system

AbstractThe Advanced Very‐High‐Resolution Radiometer (AVHRR) is a broad‐band, five‐channel scanner sensing in the visible, near‐infrared and thermal infrared portions of the electromagnetic spectrum. AVHRR instruments onboard polar‐orbiting satellites have data records spanning 30 years. The radiances from the infrared channels of AVHRR have been directly assimilated over the ice‐free ocean in the NASA Goddard Earth Observing System (GEOS) since 2017 to constrain skin sea surface temperature (SST). The GEOS system already uses an advanced bulk SST and a skin SST model which incorporates diurnal warming and cool‐skin temperature on the surface of the ocean. The positive contribution of this effort to the Numerical Weather Prediction (NWP) makes it desirable to extend the skin SST data assimilation procedure to reanalysis applications. The AVHRR data from platforms that were previously unexplored for NWP applications are assimilated at the Global Modeling and Assimilation Office (GMAO) at NASA, for an upcoming reanalysis project. This study uses results from reanalysis experiments to assess the impact of assimilating AVHRR radiances over open ocean on the atmospheric state variables, focusing on the analyzed skin temperatures that include the active skin SST model. It is demonstrated that including the skin temperature variations in atmospheric analysis is generally beneficial. The addition of radiance measurements from AVHRR provides further improvements to the overall reanalysis system performance. by helping to correct the diurnal heating and reducing errors in the background departure of hyperspectral radiance observations, which are an essential component of atmospheric reanalyses.

Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall

Ground-based microwave radiometer (GMWR) data with high spatial and temporal resolution can improve the accuracy of weather forecasts when effectively assimilated into numerical weather prediction. Nowadays, the major method to assimilate these data is via indirect assimilation by assimilating the retrieved profiles, which introduces large retrieval errors and cannot easily be represented by an error covariance matrix. Direct assimilation, on the other hand, can avoid this issue. In this study, the ground-based version of the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-gb) was selected as the observation operator, and a direct assimilation module for GMWR radiance data was established in the Weather Research and Forecasting Model Data Assimilation (WRFDA). Then, this direct assimilation module was applied to assimilate GMWR data. The results were compared to the indirect assimilation experiment and demonstrated that direct assimilation can more effectively improve the model’s initial fields in terms of temperature and humidity than indirect assimilation while avoiding the influence of retrieval errors. In addition, direct assimilation performed better in the precipitation forecast than indirect assimilation, making the main precipitation center closer to the observation. In particular, the improvement in the precipitation forecast with a threshold of 60 mm/6 h was obvious, and the corresponding TS score was significantly enhanced.

An isotopically enriched mantle component in the source of Rodrigues, Réunion volcanic hotspot

The Mascarene Islands in the western Indian Ocean, encompassing La Réunion, Mauritius, and Rodrigues, are the recent (<10 Ma) surface expressions of the Réunion hotspot. Ocean island basalts (OIB) from these islands exhibit a remarkably homogeneous long-lived radiogenic isotopic composition, coinciding with the convergence field of many global OIB trends in the mantle array. Réunion plume-related OIB therefore provide one of Earth’s most pristine representations of this “focal zone” component, which may have a primordial heritage. Besides this signature, Mascarene lavas have been suggested to retain contributions from sources with distinct compositions, including: (1) Archaean-aged zircons assimilated from continental crust within the oceanic lithosphere by trachytic magmas from Mauritius; (2) more deeply recycled continental crust components preserved by elevated 87Sr/86Sr and 208Pb/206Pb in lavas from the Piton des Neiges volcano of Réunion; and (3) an isotopically depleted mantle component resulting from interaction with Central Indian Ridge material.In this study we use Sr-Nd-Pb isotope systematics, along with major and trace element compositions of basaltic lavas from all three Mascarene Islands to investigate the relationship of their sources to well-characterized mantle endmembers. Among the Mascarene Islands, Rodrigues lavas are the most enriched in highly incompatible elements, likely reflecting shallower and lower degrees of partial melting than Réunion or Mauritius. Combined Sr-Nd-Pb isotope compositions indicate that lavas from the Older Series of Mauritius resemble those from Réunion, whereas lavas from the Younger and Intermediate Series, together with Rodrigues, are consistent with contributions from an isotopically depleted component. In addition, the Pb isotopic compositions of Rodrigues samples require an additional contribution from a component with a long-term enrichment in its Th/U ratio. Based on isotope mixing models, direct assimilation of continental crust embedded within the oceanic lithosphere is unlikely to account for the Pb isotopic variation of Rodrigues. A metasomatized mantle component, previously envisaged as a “fossil” Réunion plume near the Central Indian Ridge, is partially able to reproduce the trace element signature, but not the observed Sr-Nd-Pb isotopic compositions of Rodrigues. Instead, small proportions (<5%) of an EM1-like component provide a preferred endmember. The composition and origin of this component, which is exclusively reflected within Rodrigues lavas, may constitute a new geochemical feature of the Mascarene Islands and is consistent with geodynamical predictions that small-scale enriched mantle domains may be widespread in the source regions of hotspot volcanoes.

Assimilating Low‐Cost High‐Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach

AbstractUncertainty reduction in watershed water quality (WWQ) modeling remains a major challenge. One important reason is the lack of sufficient available water quality observations because traditional laboratory analysis of water samples has high labor, financial and time costs. Low‐cost high‐frequency water quality data from in‐situ sensors provide an opportunity to solve this problem. However, long‐term sensing in complex natural environments usually suffers more significant errors. This study aimed to develop a novel method to utilize in‐situ sensor data in WWQ modeling, namely, the Bayesian calibration using multisource observations (BCMSO), which can simultaneously assimilate laboratory‐based observations and in‐situ sensor data. Both synthetic and real‐world cases of nitrate modeling were used to demonstrate the methodology, and the Soil and Water Assessment Tool was employed as the WWQ model. The results indicated that direct assimilation of sensor data using traditional Bayesian calibration generated obvious deviations in parameter inference and model simulation, which could consequently bias future predictions and affect management decision correctness. However, after proper treatment of errors in sensor data, the BCMSO method could extract meaningful information from sensor data and prevent negative impacts of errors. The modeling uncertainty was also greatly reduced. In the real‐world case, with 1 yr of subhourly electrical conductivity sensor data incorporated, the modeling uncertainty of nitrate concentration and management cost of controlling nitrate pollution were reduced by 70%. The BCMSO method provides a flexible framework to accommodate nonconventional observations in environmental modeling and can be easily extended to other modeling fields.

The soil microbial community and plant biomass differentially contribute to the retention and recycling of urinary-N in grasslands

Urine patches in grazed systems are hotspots for nitrogen (N) cycling and losses to the wider environment. Retention and subsequent recycling of urinary-N is key to minimise losses and increase ecosystem nitrogen use efficiency. Biosynthesis into the microbial organic N pool is an important N pathway but this has not been directly quantified in a urine patch. Herein, we present the results of a time course experiment using soil mesocosms sown with perennial ryegrass (Lolium perenne L.) and treated with 15N-labelled sheep urine to determine partitioning of the applied N between plant, soil biomass pools and leaching losses following simulated rainfall events. 15N-tracing used bulk and compound-specific 15N-stable isotope probing (SIP) to determine the fate of urinary N. Initial high leaching losses (233 kg N ha−1) were comprised of native soil N, ammonium and nitrate derived from urine by urea hydrolysis and nitrification, respectively. Leaching subsequently decreased whilst uptake into plant biomass and microbial biosynthesis increased during periods of low rainfall. Uptake into above and belowground plant biomass was the largest fate of urinary-15N after 94 d (42%), although assimilation into microbial biomass dominated for ca. 1 month after urine deposition (34%). Compound-specific 15N–SIP of amino acids and amino sugars revealed immobilisation of urinary-N following mineralisation was the dominant pathway for biosynthesis, with incorporation into bacterial organic N pools more rapid than into the fungal biomass. There was also intact utilisation of glycine derived from urine. This study provides clear evidence that direct assimilation of urine-derived N into microbial organic N pools is an important process for retaining N in a urine patch, which will subsequently support plant N supply during microbial turnover.

Impact of hydrometeor control variables in a convective‐scale 3DEnVar data assimilation scheme

AbstractInitialization of condensate water variables for convective‐scale weather forecasting has been a topic of active research for the last few years. In this article, we present the addition of hydrometeor fields as control variables of a three‐dimensional ensemble variational scheme (3DEnVar) for the cloud‐resolving model AROME‐France. Even without any direct assimilation of hydrometeor observations, analysis increments of hydrometeors can be produced via covariances with observed variables in the ensemble‐derived background‐error covariance matrix. Cycled forecast–analysis experiments in near‐operational conditions have been performed over a three‐month summer period. Three configurations are compared: (i) a control experiment without hydrometeor control variables, (ii) a test experiment with hydrometeor control variables but without cycling the resulting forecasts, and (iii) an experiment with hydrometeor variables and cycled forecasts. Compared with the control experiment, both hydrometeor experiments show a positive impact up to 9 hr of forecasts in terms of cloud cover, and in the first hour for precipitation. The spin‐up period, as evidenced by a reduction of precipitation forecast skill at the beginning of the forecasts, is reduced in length and intensity in the hydrometeor experiments.

Assimilation of GRACE Follow‐On Inter‐Satellite Laser Ranging Measurements Into Land Surface Models

AbstractThe monthly mean level‐2 (L2) time‐variable gravity as well as level‐3 (L3) Mass Concentration blocks (mascons) data from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (GRACE‐FO) missions are frequently used for improving land surface models. The conventional data assimilation approach requires several pre‐processing steps like filtering (to suppress the random and systematic errors), correction (to reduce the leakage effect) and scaling (to un‐do the attenuation effect by filters) before integrating the data with the models, for example, when computing total water storage (TWS) changes from L2 data. Moreover, due to the monthly sampling of L2 and L3 data, the model estimates are updated only once a month. This confines the applications of the approaches to cases where water storage is slowly varying at seasonal or interannual time‐scales. We present a new methodology based on direct assimilation of along‐orbit Line‐of‐sight Gravity Difference (LGD) measurements from the GRACE‐FO laser ranging interferometer (LRI) which overcomes the limitation of the conventional approach. Inter‐satellite ranging data reflect the gravitational changes at satellite altitude caused by the instantaneous TWS changes at the Earth surface. Therefore, they pose information at wide‐ranging time‐scales from hours to multiple years. The proposed method is applied globally to assimilate LRI data into a land surface model. Evaluation against multiple satellite‐based and ground data shows the superiority of our proposed approach. The new approach also offers better performance in capturing high‐frequency water storage variations imposed by sub‐monthly climatic events due to its higher number of data assimilation cycles within a month.

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.

Effects of joint assimilation of FY-4A AGRI and ground-based microwave radiometer on heavy rainfall prediction

As the latest generation of Chinese Geostationary Weather Satellites, Fengyun-4 carries the Advanced Geosynchronous Radiation Imager (AGRI), which has more spectral bands and higher temporal and spatial resolution than the Visible Infrared Spin-Scan Radiometer (VISSR) onboard geostationary satellite FY-2. Direct assimilation of the FY-4A AGRI datasets has been proved to be an efficient way to improve heavy rainfall simulation. We aim to assess the joint assimilation of AGRI infrared radiance and ground-based MWR (Microwave Radiometer) data on short-duration heavy rainfall prediction. RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) is used as the observational operator for FY-4A AGRI data assimilation. The data assimilation interface is built in WRFDA 4.3 to achieve direct assimilation of FY4A AGRI radiance. The forecasting effectiveness of the joint assimilation for a typical heavy rainfall event over northern China is analyzed with four simulation experiments. The main conclusions are: 1) Assimilating MWR data can improve the initial humidity condition in the middle-lower layers, while AGRI radiance assimilation favors the initial humidity correction in the middle-upper layers. The joint assimilation of two datasets can remarkably improve the initial humidity condition in the entire column. 2) Data assimilation effectively improves the 6-h accumulated rainfall simulation. The joint assimilation of AGRI radiance and MWR data is superior to assimilating either of them. The joint assimilation significantly improves the rainfall forecast over the Beijing area, where the seven MWRs are distributed. 3) Data assimilation experiments present similar effects on predicted and initial humidity conditions. The MWR_DA experiment (only assimilate MWR data) markedly improves the humidity forecast in the middle-lower layers, while AGRI_DA (only assimilate AGRI data) is effective in the middle-upper layers. The joint assimilation of AGRI radiance and MWR data could skillfully correct the humidity distribution in the entire layers, allowing for more accurate heavy rainfall prediction. This paper provides a valuable basis for further improving the application of FY-4A AGRI radiance in numerical weather models.

How aerosol size matters in aerosol optical depth (AOD) assimilation and the optimization using the Ångström exponent

Abstract. Satellite-based aerosol optical depth (AOD) has gained popularity as a powerful data source for calibrating aerosol models and correcting model errors through data assimilation. However, simulated airborne particle mass concentrations are not directly comparable to satellite-based AODs. For this, an AOD operator needs to be developed that can convert the simulated mass concentrations into model AODs. The AOD operator is most sensitive to the input of the particle size and chemical composition of aerosols. Furthermore, assumptions regarding particle size vary significantly amongst model AOD operators. More importantly, satellite retrieval algorithms rely on different size assumptions. Consequently, the differences between the simulations and observations do not always reflect the actual difference in aerosol amount. In this study, the sensitivity of the AOD operator to aerosol properties has been explored. We conclude that, to avoid inconsistencies between the AOD operator and retrieved properties, a common understanding of the particle size is required. Accordingly, we designed a hybrid assimilation methodology (hybrid AOD assimilation) that includes two sequentially conducted procedures. First, aerosol size in the model operator has been brought closer to the assumption of the satellite retrieval algorithm via assimilation of Ångström exponents. This ensures that the model AOD operator is more consistent with the AOD retrieval. The second step in the methodology concerns optimization of aerosol mass concentrations through direct assimilation of AOD (standard AOD assimilation). The hybrid assimilation method is tested over the European domain using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue products. The corrections made to the model aerosol size information are validated through a comparison with the ground-based Aerosol Robotic Network (AERONET) optical product. The increments in surface aerosol mass concentration that occur due to either the standard AOD assimilation analysis or the hybrid AOD assimilation analysis are evaluated against independent ground PM2.5 observations. The standard analysis always results in relatively accurate posterior AOD distributions; however, the corrections are hardly transferred into better aerosol mass concentrations due to the uncertainty in the AOD operator. In contrast, the model AOD and mass concentration states are considerably more accurate when using the hybrid methodology.