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Satellite infrared (IR) sounder and imager measurements have become one of the main sources of data used by data assimilation systems to generate initial conditions for numerical weather prediction (NWP) models and atmospheric analysis/reanalysis. This paper reviews the development of satellite IR data assimilation in NWP in recent years, especially the assimilation of all-sky satellite IR observations. The major challenges and future directions are outlined and discussed.

ABSTRACTThe advanced infrared (IR) and microwave (MW) sounding systems have been providing atmospheric sounding information critical for nowcasting and improving weather forecasts through data assimilation in numerical weather prediction. In recent years, advanced IR and MW sounder systems are being proposed to be onboard CubeSats that are much more cost efficient than traditional satellite systems. An impact study using a regional Observing System Simulation Experiment on a local severe storm (LSS) was carried out to evaluate the alternative of using advanced MW and IR sounders for high-impact weather forecasting in mitigating the potential data gap of the Advanced Technology Microwave Sounder (ATMS) and the Cross-track Infrared Sounder (CrIS) on the Suomi-NPP (SNPP) or Joint Polar Satellite System (JPSS). It was found that either MicroMAS-2 or the CubeSat Infrared Atmospheric Sounder (CIRAS) on a single CubeSat was able to provide a positive impact on the LSS forecast, and more CubeSats with increased data coverage yielded larger positive impacts. MicroMAS-2 has the potential to mitigate the loss of ATMS, and CIRAS the loss of CrIS, on SNPP or JPSS, especially when multiple CubeSats are launched. There are several approximations and limitations to the present study, but these represent efficiencies appropriate to the principal goal of the study — gauging the relative values of these sensors.

Many research and societal applications such as surface solar irradiance assessment and forecasting require accurate short-term cloudiness forecasts at kilometre and hourly scales. Today limited-area numerical weather prediction models have the potential to provide such forecasts by simulating clouds at high spatial and temporal resolutions. However, the forecast performance during the first 12-24 h is strongly influenced by the accuracy of the cloud and thermodynamic analyses in the initial conditions. Geostationary meteorological satellites provide valuable observations that can be used in data assimilation for frequent cloud analysis determination. This paper provides an up-to-date review of the state of the art in cloud-related geostationary satellite data assimilation with limited-area models dedicated to improve cloudiness forecast performance. Research and operational studies have been reviewed by differentiating between satellite radiance and cloud property retrieval assimilation. This review gives insight into the best practices considering the large variety of limited-area models, data assimilation methods, satellite sensors and channels, cloud property retrieval products and various methodological challenges. Cloud analysis methods for regional models have become more sophisticated in recent years and are increasingly able to exploit observations from geostationary satellites. Important proofs of concept have been performed in this decade, paving the way for an optimal synergy of geostationary satellite data assimilation and convection-permitting limited-area model forecasts. At the same time, the increasing amount of channels of geostationary satellite instruments leads to more opportunities and challenges for data assimilation methods. [ABSTRACT FROM AUTHOR]

The Community Radiative Transfer Model (CRTM) presently uses one look‐up table (LUT) of cloud and precipitation single‐scattering properties at microwave frequencies, with which any particle size distribution may interface via effective radius. This may produce scattering properties insufficiently representative of the model output if the microphysics parameterization scheme particle size distribution mismatches that assumed in constructing the LUT, such as one being exponential and the other monodisperse, or assuming different particle bulk densities. The CRTM also assigns a 5 μm effective radius to all nonprecipitating clouds, an additional inconsistency. Brightness temperatures are calculated from 3 h convection‐permitting simulations of Hurricane Karl (2010) by the Weather Research and Forecasting model; each simulation uses one of three different microphysics schemes. For each microphysics scheme, a consistent cloud scattering LUT is constructed; the use of these LUTs produces differences in brightness temperature fields that would be better for analyzing and constraining microphysics schemes than using the CRTM LUT as‐released. Other LUTs are constructed which contain one of the known microphysics inconsistencies with the CRTM LUT as‐released, such as the bulk density of graupel, but are otherwise microphysics‐consistent; differences in brightness temperature to using an entirely microphysics‐consistent LUT further indicate the significance of that inconsistency. The CRTM LUT as‐released produces higher brightness temperature than using microphysics‐consistent LUTs. None of the LUTs can produce brightness temperatures that can match well to observations at all frequencies, which is likely due in part to the use of spherical particle scattering. The CRTM is modified to use microwave scattering look‐up tables made consistent with cloud/precipitation properties of microphysics schemesThe CRTM as‐released, using effective radius to specify cloud particle sizes, produces higher brightness temperatures than the modified CRTMBrightness temperatures are sensitive to the assumed bulk density of graupel and ice cloud particle sizes produced by microphysics schemes

Flash droughts evolve and intensify rapidly under the influence of anomalous atmospheric conditions. In this study, we investigate the role of assimilating remotely sensed soil moisture (SM) and vegetation properties in capturing the evolution and impacts of two flash droughts in the Northern Great Plains. We find that during 2016 drought triggered by anomalously high temperatures and excessive evaporative demands, multivariate data assimilation (DA) of MODIS‐derived leaf area index (LAI) and Soil Moisture Active Passive SM within Noah‐Multiparameterization model helps capture elevated transpiration at onset. Assimilation of LAI particularly helped model the resulting rapid decline in SM during onset with as high as 10.0% steeper rate of decline compared to the simulation without any assimilation. Modeled‐SM anomalies exhibit a 7.5% and 11.7% increase in similarity with Evaporative Stress Index (ESI) data and U.S. Drought Monitor (USDM) maps, respectively. In contrast, during 2017 flash drought driven by record‐low precipitation during summers, SM assimilation resulted in largest rates of decline in rootzone SM, as large as 48.4% compared to results from no assimilation. Multivariate DA of SM and LAI results in 6.7% and 14.3% higher spatial similarity with ESI and USDM, respectively, and is necessary to model rapid intensification caused by anomalous precipitation deficits. This study elucidates the need to incorporate multiple observational constraints from remote sensing to effectively capture rapid onset rates, intensification, and severity of flash drought following different propagation mechanisms. This is fundamental for drought early detection to provide a wider window of response and implement efficient mitigation strategies. A class of droughts called flash droughts develop rapidly under unusual weather conditions, often characterized by either warm temperatures or low precipitation or both. In this study, we employ the soil moisture (SM) and leaf area index (LAI) retrievals from the NASA Soil Moisture Active Passive mission and MODIS product, respectively, for characterizing the flash droughts of 2016 and 2017 in the Northern Great Plains. The results demonstrate that LAI observations, when assimilated within a land surface model, are effective in capturing high transpiration at the onset of 2016 drought driven by intense heat waves. The 2017 flash drought, however, was initiated by a precipitation deficit where information on SM is necessary to capture the rapid drying of soils. The modeled outputs not only capture the rapid drying of soil at the onset of droughts but are also spatially and temporally consistent with Evaporative Stress Index data and U.S. Drought Monitor maps. The study highlights the role of multivariate assimilation of remotely sensed vegetation and SM information to capture the rapid rates of onset and contrasting pathways of flash drought development. Multivariate assimilation of remotely sensed vegetation and soil moisture helps characterize recent flash droughts in Northern Great PlainsHeatwave‐driven warm flash drought requires assimilating vegetation conditions to capture impact on transpiration during rapid developmentUse of soil moisture data is necessary to represent rapid drying of soils during the dry flash drought intensified by moisture deficit Multivariate assimilation of remotely sensed vegetation and soil moisture helps characterize recent flash droughts in Northern Great Plains Heatwave‐driven warm flash drought requires assimilating vegetation conditions to capture impact on transpiration during rapid development Use of soil moisture data is necessary to represent rapid drying of soils during the dry flash drought intensified by moisture deficit

The influence of the Unified Noah and Noah‐MP land surface models (LSMs) on the evolution of cumulus clouds reaching convective initiation (CI) is assessed using infrared brightness temperatures (BT) from GOES‐16. Cloud properties from individual cloud objects are examined using output from high‐resolution (500 m horizontal grid spacing) model simulations. Cloud objects are tracked over time and related to observed clouds reaching CI to examine differences in cloud extent, longevity, and growth rate. The results demonstrate that differences in assumed surface properties can lead to large discrepancies in the net surface radiative budget, particularly in the sensible and latent heating components where differences exceed 40 W m−2. These differences lead to changes in the local mesoscale circulation patterns that are more pronounced near the edges of forested and grassland boundaries where lower‐level convergence is stronger. Higher sensible heating from the Noah‐MP LSM produced growth of CI clouds earlier and with increased longevity, which was closer to the timing and growth observed from GOES‐16. The increased cloud growth in the Noah‐MP experiment results from stronger and deeper updrafts, which lofts more cloud water into the upper levels of the troposphere. The weaker updrafts from the Noah LSM experiment results in shallower convection after CI is detected due to slower growth rates. The differences in cloud properties and growth are directly related to the land surfaces they develop above and point to the importance of accurately representing land properties and radiative characteristics when simulating convection in numerical weather prediction models. Weather prediction models consist of many different parameters and assumptions. In this study, we compared how assumptions of the land surface impact the growth of cumulus clouds and thunderstorms across the southeastern United States. It was found that differences in the land surface schemes can directly impact the local circulations where cumulus clouds and convective storms develop; this leads to differences in how large the clouds grow and sustain over time. By tracking clouds using an object‐based methodology, we were able to compare growth characteristics to those observed by geostationary satellites. The model and satellite comparisons helped demonstrate that the cloud growth is quite sensitive to the model interpretation of surface energy balances, particularly over heterogeneous landscapes containing forests and grasslands. The differences in the amount of energy transferred from the surface to the atmosphere lead to downstream differences in cloud updraft strength. These differences in cumulus cloud characteristics influence the formation of ice in the upper levels of clouds, which is essential in the convective storm initiation process. The comparison with satellite data provided the ability to validate cloud growth and further understand the processes leading to longer‐lived thunderstorms. The Noah‐MP LSM leads to a more accurate size distribution and growth rate of convection compared to the Noah LSMThe latent and sensible heating components of the surface radiation balance drive differences in local circulation patternsSurface energy imbalances impact updraft characteristics, leading to downstream influences of cloud growth and sustained convection The Noah‐MP LSM leads to a more accurate size distribution and growth rate of convection compared to the Noah LSM The latent and sensible heating components of the surface radiation balance drive differences in local circulation patterns Surface energy imbalances impact updraft characteristics, leading to downstream influences of cloud growth and sustained convection

Infrared brightness temperatures (BTs) from the Geostationary Observing Environmental Satellite‐16 Advanced Baseline Imager are used to examine the ability of several microphysics and planetary boundary layer (PBL) schemes, as well as land surface models (LSM) and surface layers, to simulate upper‐level clouds. Six parameterization configurations were evaluated. Cloud objects are identified using the Method for Object‐Based Diagnostic Evaluation (MODE) and analyzed using the object‐based threat score, mean‐error distance, and pixel‐based metrics including the mean absolute error and mean bias error (MBE) for matched objects where the displacement between objects has been removed. Objects are identified using either a fixed BT threshold of 235 K or the 6.5th percentile of BTs for each model configuration. Analysis of the MODE‐identified cloud objects shows that, compared to a configuration with the Thompson microphysics scheme, Mellor‐Yamanda‐Nakanishi‐Niino (MYNN) PBL, Global Forecasting System (GFS) surface layer, and Noah LSM, the configuration employing the National Severe Storms Laboratory microphysics produced more cloud objects with higher BTs. Changing the PBL from MYNN to Shin‐Hong or Eddy‐Diffusivity Mass‐Flux also resulted in a slightly lower accuracy, though these changes result in configurations which more accurately reproduced the number of observation cloud objects and slightly reduced the high MBE. Changing the LSM from Noah to RUC reduces forecast accuracy by producing too many cloud objects with too low BTs. As the forecast hour increases, this accuracy reduction increases at a greater rate than occurred when changing the microphysics or PBL scheme and is further enhanced when using the MYNN surface layer rather than the GFS. Weather prediction models consist of many different parameters, which impact the resulting forecast. In this paper, we compared cloud forecasts generated using different model parameters to represent cloud processes, the bottom layer of the atmosphere, and the model surface. These model parameters are being considered for inclusion in future operational versions. It was found, the changes to the microphysics scheme dictating cloud processes had the largest impact on the accuracy of cloud forecasts, with the Thompson scheme producing more accurate clouds than the National Severe Storms Laboratory scheme. Cloud forecasts were more accurate when the bottom layer of the atmosphere was defined using the Mellor‐Yamanda‐Nakanishi‐Niino (MYNN) scheme instead of Shin‐Hong or an Eddy‐Diffusivity Mass‐Flux scheme. Changing the land surface model from Noah to RUC also reduced cloud forecast accuracy. This reduction in accuracy increases as the forecast hour increases, at a greater rate than occurred when changing the microphysics scheme or the scheme defining the bottom layer of the atmosphere. This reduction is further enhanced when using the MYNN surface layer rather than the Global Forecasting System. Thompson microphysics scheme has the most accurate upper‐level simulated clouds, with National Severe Storms Laboratory producing too fewChanging the planetary boundary layer scheme from Mellor‐Yamanda‐Nakanishi‐Niino (MYNN) to Shin‐Hong or Eddy‐Diffusivity Mass‐Flux resulted in slightly lower cloud accuracyRUC land surface model produces too many clouds compared to Noah, and is further enhanced when using the MYNN surface layer instead of Global Forecasting System Thompson microphysics scheme has the most accurate upper‐level simulated clouds, with National Severe Storms Laboratory producing too few Changing the planetary boundary layer scheme from Mellor‐Yamanda‐Nakanishi‐Niino (MYNN) to Shin‐Hong or Eddy‐Diffusivity Mass‐Flux resulted in slightly lower cloud accuracy RUC land surface model produces too many clouds compared to Noah, and is further enhanced when using the MYNN surface layer instead of Global Forecasting System

Due to the influence of evaporation on land‐surface temperature, thermal remote sensing data provide valuable information regarding the surface moisture status. The Atmosphere‐Land Exchange Inverse (ALEXI) model uses the morning surface temperature rise, as measured from a geostationary satellite platform, to deduce surface energy and water fluxes at 5–10 km resolution over the continental United States. Recent improvements to the ALEXI model are described. Like most thermal remote sensing models, ALEXI is constrained to work under clear‐sky conditions when the surface is visible to the satellite sensor, often leaving large gaps in the model output record. An algorithm for estimating fluxes during cloudy intervals is presented, defining a moisture stress function relating the fraction of potential evapotranspiration obtained from the model on clear days to estimates of the available water fraction in the soil surface layer and root zone. On cloudy days, this stress function is inverted to predict the soil and canopy fluxes. The method is evaluated using flux measurements representative at the watershed scale acquired in central Iowa with a dense flux tower network during the Soil Moisture Experiment of 2002 (SMEX02). The gap‐filling algorithm reproduces observed fluxes with reasonable accuracy, yielding ∼20% errors in ET at the hourly timescale, and 15% errors at daily timesteps. In addition, modeled soil moisture shows reasonable response to major precipitation events. This algorithm is generic enough that it can easily be applied to other thermal energy balance models. With gap‐filling, the ALEXI model can estimate hourly surface fluxes at every grid cell in the U.S. modeling domain in near real‐time. A companion paper presents a climatological evaluation of ALEXI‐derived evapotranspiration and moisture stress fields for the years 2002–2004.