All-sky Radiances Research Articles

The Predictability of a Heavy Rainfall Event during the Summer of 2022 Using an All-sky Radiance Assimilation Experiment

This paper presents the results of the recent development of the all-sky radiance assimilation system in the Korean Integrated Model (KIM). In the cycled analysis and forecast experiments, the increased coverage of radiance data in cloudy regions improved the quality of initial fields for mass variables, temperature and humidity. The experimental period covered the record-breaking heavy rainfall event on August 9, 2022. We examined the simulation accuracy of the western North Pacific subtropical high (WNPSH) in both clear- and all-sky experiments. In the clear-sky experiment, northward propagation of the WNPSH was restricted. A humid bias exists with clear-sky radiance assimilation over the WNPSH region. Since humid air is lighter than dry air, in this situation, the geopotential height (GPH) should be lower to achieve the same pressure, and a low-pressure bias occurs. All-sky radiance assimilation dries the moisture field, which helps elevate the GPH over the WNPSH region. The expansion of the WNPSH yielded a steeper confrontation in the air between the land and ocean around the southeastern sea of the Korean Peninsula to predict the strength of rainfall events more accurately. A more accurate simulation of the jet stream outlet was also demonstrated in an all-sky experiment. This study shows that the all-sky radiance assimilation can help to more accurately predict extreme rainfall events via proper simulations of large-scale fields.

All‐sky assimilation of FY‐4A AGRI water vapor channels: An observing system experiment study for south Asian monsoon prediction

AbstractIn this study, a month‐long parallel experiment is conducted to assess the assimilation effects of all‐sky radiance (ASR) versus clear‐sky radiance (CSR) in the single and multiple water vapor (WV) channels provided by the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun‐4A (FY‐4A) satellite. The Weather Research and Forecasting model data assimilation system (WRFDA) is utilized for assimilating the FY‐4A AGRI WV observations. Compared with the CSR, the assimilated data of the ASR increases by ~1.7 times in WV channel 9 and around 2.4 times in the multiple WV channels (channels 9 and 10). Both the CSR and ASR assimilation results demonstrate that the simulated brightness temperature (TB) from the WRF analyses is closer to the TB observations from Sondeur Atmospherique du Profil d'Humidite Intertropicale par Radiometrie (SAPHIR) channels 1–2 and Microwave Humidity Sounder (MHS) channel 3, as compared with the control experiment where only conventional data are assimilated. Furthermore, a larger improvement is noted in assimilation of multiple WV channel against assimilation of a single WV channel for both CSR and ASR runs. Overall, the analyses and forecasts generated from the ASR run exhibit superior performance compared with the CSR run when verified against SAPHIR and MHS measurements, as well as the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analyses. The verification of the rainfall predicted by the WRF against the Global Satellite Mapping of Precipitation (GSMaP) products over India indicates that the assimilation of the ASR in multiple WV channels has the largest impact on rainfall prediction. Moreover, the ASR run demonstrates an accurate prediction of heavy rainfall events that are missed in the control experiment and underestimated in the CSR run.

Assimilation of GOES-16 ABI All-Sky Radiance Observations in RRFS Using EnVar: Methodology, System Development, and Impacts for a Severe Convective Event

Abstract The Advanced Baseline Imager (ABI) aboard the GOES-16 and GOES-17 satellites provides high-resolution observations of cloud structures that could be highly beneficial for convective-scale DA. However, only clear-air radiance observations are typically assimilated at operational centers due to a variety of problems associated with cloudy radiance data. As such, many questions remain about how to best assimilate all-sky radiance data, especially when using hybrid DA systems such as EnVar wherein a nonlinear observation operator can lead to cost function gradient imbalance and slow minimization. Here, we develop new methods for assimilating all-sky radiance observations in EnVar using the novel Rapid Refresh Forecasting System (RRFS) that utilizes the Finite-Volume Cubed-Sphere (FV3) model. We first modify the EnVar solver by directly including brightness temperature (Tb) as a state variable. This modification improves the balance of the cost function gradient and speeds up minimization. Including Tb as a state variable also improves the model fit to observations and increases forecast skill compared to utilizing a standard state vector configuration. We also evaluate the impact of assimilating ABI all-sky radiances in RRFS for a severe convective event in the central Great Plains. Assimilating the radiance observations results in better spinup of a tornadic supercell. These data also aid in suppressing spurious convection by reducing the snow hydrometeor content near the tropopause and weakening spurious anvil clouds. The all-sky radiance observations pair well with reflectivity observations that remove primarily liquid hydrometeors (i.e., rain) closer to the surface. Additionally, the benefits of assimilating the ABI observations continue into the forecast period, especially for localized convective events.

All‐sky infrared radiance assimilation of a geostationary satellite in the Japan Meteorological Agency's global system

AbstractAll‐sky assimilation of infrared (IR) radiances has been developed for water vapor bands of the geostationary satellite Himawari‐8 in the operational global data assimilation system. Cloud‐dependent quality control, bias correction, and observation error modeling are essential developments to effectively utilize the all‐sky radiances (ASRs). ASR assimilation increases the assimilated number of observations by 2.8 times and improves the coverage relative to the traditional clear‐sky radiance (CSR) assimilation. The additional observations better alleviate model dry biases in the middle and upper tropospheric humidity. ASR assimilation brings statistically significant improvements in the background (first guess) in humidity, temperature, and wind over the CSR assimilation. It also better improves short‐range forecasts of the middle and upper tropospheric temperature and humidity up to day 3 in the Tropics. A mixed impact in the stratospheric temperature is under investigation. The impacts of various aspects of the ASR assimilation configuration are evaluated with sensitivity assimilation experiments. The interband correlation and cloud‐dependent standard deviation of the observation error are crucial, whereas the cloud dependency of the correlation is not so important. Although ASRs at a single band were assimilated in many previous studies targeting severe weather using research‐based regional assimilation systems due to decreasing independent information in the presence of clouds, they are distinctly inferior to not only ASRs at multiple bands but also CSRs at multiple bands in a global data assimilation system that contains fewer cloud‐affected scenes. The cloud‐dependent bias correction predictors are essential in the presence of observation‐minus‐background bias that increases with cloud effects.

Impact of Assimilating GK-2A All-Sky Radiance with a New Observation Error for Summer Precipitation Forecasting

In the assimilation of all-sky radiance (ASR), the non-Gaussian behaviour of observation-minus-background (OMB) departures has been the major issue. Treating observation error properly should give the distribution OMB departures closer to Gaussian on which data assimilation systems are based. This study introduces a look-up-table (LUT) observation error inflation (LOEI) for assimilating ASR from three water vapor channels of GEO-KOMPSAT-2A (GK-2A) geostationary satellite based on a three-dimensional variational data assimilation (3DVAR) framework. The impacts are assessed based on summer precipitation cases over South Korea. To confirm all kinds of radiance observations, the ASRs are assimilated without any quality control procedures. The LOEI adopt a pre-estimated radiance error statistics by using the higher order fitting function of cloud amount (CA) and standard deviation (STD) of OMB departures. This LOEI was produced during the summer period from August 1 to 30, 2020, representing the characteristics of the atmosphere condition during the experimental period. The promising impact of LOEI is demonstrated in comparison with the inflated observation error using a simple linier function proposed by Geer and Bauer (GBOEI). Study results revealed the LOEI normalized OMB departures into much more Gaussian form than the GBOEI. Hence, the assimilation of ASR using LOEI (ExpLOEI) produced BT analysis closer to the observation in four cloud phases in contrast with ASR assimilation using GBOEI (ExpGBOEI), which obviously found in the ice phase. The better BT analysis eventually simulated more realistic moisture and temperature variables in the background field. Consequently, the ExpLOEI exhibited more accuracy in precipitation location and intensity compared to the experiment with ExpGBOEI.

Ground-based measurements of the weather-driven sky radiance distribution in the Southern Hemisphere.

The angular distribution of the sky radiance determines the energy generation of solar power technologies as well as the ultraviolet (UV) doses delivered to the biosphere. The sky-diffuse radiance distribution depends on the wavelength, the solar elevation, and the atmospheric conditions. Here, we report on ground-based measurements of the all-sky radiance at three sites in the Southern Hemisphere across a transect of about 5,000 km: Santiago (33°S, a mid-latitude city of 6 million inhabitants with endemic poor air quality), King George Island (62°S, at the northern tip of the Antarctic Peninsula, one of the cloudiest regions on Earth), and Union Glacier (79°S, a snow-covered glacier in the vast interior of Western Antarctica). The sites were strategically selected for studying the influence of urban aerosols, frequent and thick clouds, and extremely high albedo on the sky-diffuse radiance distribution. Our results show that, due to changing site-specific atmospheric conditions, the characterization of the weather-driven sky radiance distribution may require ground-based measurements.

Assimilating All-Sky Infrared Radiance Observations to Improve Ensemble Analyses and Short-Term Predictions of Thunderstorms

The experimental rapid-cycling Ensemble Kalman Filter (EnKF) in the convection-allowing ensemble-based Warn-on-Forecast System (WoFS) at the National Severe Storms Laboratory (NSSL) is used to assimilate all-sky infrared radiance observations from the GOES-16 7.3 μm water vapor channel in combination with radar wind and reflectivity observations to improve the analysis and subsequent forecast of severe thunderstorms (which occurred in Oklahoma on 2 May 2018). The method for radiance data assimilation is based primarily on the version used in WoFS. In addition, the methods for adaptive observation error inflation and background error inflation and the method of time-expanded sampling are also implemented in two groups of experiments to test their effectiveness and examine the impacts of radar observations and all-sky radiance observations on ensemble analyses and predictions of severe thunderstorms. Radar reflectivity observations and brightness temperature observations from the GOES-16 6.9 μm mid-level troposphere water vapor channel and 11.2 μm longwave window channel are used to evaluate the assimilation statistics and verify the forecasts in each experiment. The primary findings from the two groups of experiments are summarized: (i) Assimilating radar observations improves the overall (heavy) precipitation forecast up to 5 (4) h, according to the improved composite reflectivity forecast skill scores. (ii) Assimilating all-sky water vapor infrared radiance observations from GOES-16 in addition to radar observations improves the brightness temperature assimilation statistics and subsequent cloud cover forecast up to 6 h, but the improvements are not significantly affected by the adaptive observation and background error inflations. (iii) Time-expanded sampling can not only reduce the computational cost substantially but also slightly improve the forecast.

Improving Radar Data Assimilation Forecast Using Advanced Remote Sensing Data

Assimilating the proper amount of water vapor into a numerical weather prediction (NWP) model is essential in accurately forecasting a heavy rainfall. Radar data assimilation can effectively initialize the three-dimensional structure, intensity, and movement of precipitation fields to an NWP at a high resolution (±250 m). However, the in-cloud water vapor amount estimated from radar reflectivity is empirical and assumes that the air is saturated when the reflectivity exceeds a certain threshold. Previous studies show that this assumption tends to overpredict the rainfall intensity in the early hours of the prediction. The purpose of this study is to reduce the initial value error associated with the amount of water vapor in radar reflectivity by introducing advanced remote sensing data. The ongoing research shows that errors can be largely solved by assimilating satellite all-sky radiances and global positioning system radio occultation (GPSRO) refractivity to enhance the moisture analysis during the cycling period. The impacts of assimilating moisture variables from satellite all-sky radiances and GPSRO refractivity in addition to hydrometeor variables from radar reflectivity generate proper amounts of moisture and hydrometeors at all levels of the initial state. Additionally, the assimilation of satellite atmospheric motion vectors (AMVs) improves wind information and the atmospheric dynamics driving the moisture field which, in turn, increase the accuracy of the moisture convergence and fluxes at the core of the convection. As a result, the accuracy of the timing and intensity of a heavy rainfall prediction is improved, and the hourly and accumulated forecast errors are reduced.

Impacts of Observation Forward Operator on Infrared Radiance Data Assimilation with Fine Model Resolutions

Abstract All-sky radiance assimilation often has non-Gaussian observation error distributions, which can be exacerbated by high model spatial resolutions due to better resolved nonlinear physical processes. For ensemble Kalman filters, observation ensemble perturbations can be approximated by the linearized observation operator (LinHx) that uses the observation operator Jacobian of ensemble mean rather than the full observation operator (FullHx). The impact of observation operator on infrared radiance data assimilation is examined here by assimilating synthetic radiance observations from channel 1025 of GIIRS with increased model spatial resolutions from 7.5 km to 300 m. A tropical cyclone is used, while the findings are expected to be generally applied. Compared to FullHx, LinHx provides larger magnitudes of correlations and stronger corrections around observation locations, especially when all-sky radiances are assimilated at fine model resolutions. For assimilating clear-sky radiances with increasing model resolutions, LinHx has smaller errors and improved vortex intensity and structure than FullHx. But when all-sky radiances are assimilated, FullHx has advantages over LinHx. Thus, for regimes with more linearity, LinHx provides stronger correlations and imposes more corrections than FullHx; but for regimes with more nonlinearity, LinHx provides detrimental non-Gaussian prior error distributions in observation space, unrealistic correlations, and overestimated corrections, compared to FullHx. Significance Statement Assimilating satellite radiances has been essential for numerical weather prediction. All-sky radiance assimilation can improve the analyses and forecasts of tropical cyclones, but it often has non-Gaussian observation error distributions. With increased model resolutions, nonlinear physical processes can be better resolved, which leads to non-Gaussian error distributions of state variables. Nonlinearity and non-Gaussianity impose great challenges for data assimilation (DA), since most DA theories assume linear processes and Gaussian error distributions. As the spatial resolutions of observations and numerical models will keep increasing, the potential issues of assimilating high-spatial-resolution observations given fine model resolutions need to be examined. The linearized observation forward operator, as an alternative to remedy non-Gaussianity for radiance DA, is investigated. With model resolution increasing from 7.5 km to 300 m, the linearized observation operator has advantages over the full observation operator for assimilating clear-sky radiances, but the opposite is true for assimilating all-sky radiances.

Assimilation of All-Sky Radiance from the FY-3 MWHS-2 with the Yinhe 4D-Var System

Assimilation of All-Sky Radiance from the FY-3 MWHS-2 with the Yinhe 4D-Var System

Improving the prediction of heavy rainfall with rapid-update dual-resolution hybrid En3DVar assimilation of all-sky AHI infrared water vapor radiances

The Advanced Himawari Imager (AHI) on board Himawari-8 provides continuous high-resolution observations of severe weather phenomena in space and time, providing a good data source for the rapidly updated cycling data assimilation in numerical weather prediction (NWP). In this study, the all-sky AHI water vapor infrared radiances were rapidly assimilated using a dual-resolution hybrid EnVar method to introduce the flow-dependent ensemble information with a reduced ensemble cost in a case study. Considering the greatly increased computational costs for all-sky radiance data assimilation, the sensitivity of the all-sky radiance data assimilation to the resolution of ensemble error covariance as well as the cycling frequency using dual-resolution EnVar was investigated. The impact of all-sky radiance data assimilation using the dual-resolution hybrid EnVar was then compared to the clear-sky radiance data assimilation.From the case study, it was found that the dual-resolution hybrid EnVar and the full-resolution hybrid EnVar generally performed comparable for the all-sky assimilation of the AHI water vapor radiances in this study. Both show great advantages over 3DVar in analyses and forecasts with different leading time for model state variables. However, the former cost much fewer computational resources with only a tiny loss of the accuracy. For the assimilation frequency of all-sky radiances using the dual-resolution hybrid EnVar, it was found that the half-hourly and hourly test both obtain the better results than the other two (namely the 10-minutely test and 3-hourly test). The former two have the comparable performances in rainfall forecast although the half-hourly test is better. Finally, in the dual-resolution hybrid EnVar framework, the all-sky AHI radiance assimilation impact was studied versus the clear-sky radiances as well as the conventional observations in a case study. The results present a generally positive improvement of the rainfall forecast with the larger rainfall thresholds, caused by the assimilation of the cloud-influenced AHI water vapor radiances.

Evaluation of Modeled Hyperspectral Infrared Spectra Against All-Sky AIRS Observations Using Different Cloud Overlap Schemes.

Hyperspectral infrared sounding contains information about clouds, which plays an important role in modulating Earth's climate. However, there is a great deal of uncertainty in modeling the radiative effect of clouds due to its complex dependence on various parameters. Therefore, cloudy scenarios are often neglected in retrievals of infrared spectral measurements and in data assimilation. One‐dimensional radiative transfer (RT) models have a limited capability to represent the cloud three‐dimensional multilayer structure. This issue is typically resolved by using a multiple independent column approach, which is computationally demanding. Therefore, it is necessary to find a balance between computational speed and accuracy for infrared RT all‐sky radiance simulations. In this study, we utilize the Community Radiative Transfer Model with four different cloud overlap schemes and compare against observations made by the Atmospheric Infrared Sounder (AIRS) using a statistical metric called the first Wasserstein distance. Our results show that the average cloud overlap scheme performs the best and successfully predicts the overall probability distribution of brightness temperature over nonfrozen oceans for a wide range of wavelengths. The mean absolute differences are less than 0.7 K for 846 selected AIRS channels between 790 cm−1 and 1231 cm−1.

Preliminary evaluation of all-sky radiance assimilation scheme with POD-3DEnVar method

To meet the challenges in satellite data assimilation involving cloud and precipitation processes, an all-sky radiance assimilation scheme (referred to as the ‘scheme’) that includes hydrometeor analysis is constructed based on the Proper Orthogonal Decomposition (POD)-3DEnVar method. Three key elements of the scheme are: (1) four hydrometeor mixing ratios for cloud-water, cloud-ice, rain, and snow in the control variables of the POD-3DEnVar method; (2) an extension of the Community Radiative Transfer Model interface to include hydrometeor profiles; and (3) multiple physical process model forecasts that use multiple microphysical and cumulus parameterization schemes as ensemble samples to estimate the flow-dependent background error covariance of the control variables, including those for hydrometeors.To evaluate the performance of the scheme, single-observation experiments and observation system simulation experiments (OSSEs) were designed for a binary typhoon case. Results for the single observation experiments show that the scheme can accurately assimilate satellite radiance near typhoon centers and produce reasonable increments for the hydrometeor variables. The background error covariances of the hydrometeor variables have reasonable flow-dependent characteristics. Results for the OSSEs have root-mean-square errors for all control variables of the assimilation analysis field with respect to the ‘true’ field that are lower than those obtained without assimilation. This indicates that the scheme effectively reduces error for all control variables, including those for hydrometeors. Forecasting experiments reveal that these improvements in the assimilation of control variables result in improved forecasts. However, improvements in the hydrometeor variables are of shorter duration than are those of the other control variables. The impact of improvements in the assimilation on typhoon track and intensity are obvious in the forecasting field.

Towards a global map of the artificial all-sky brightness

ABSTRACT Modeling the hemispherical night sky brightness of anthropogenic origin is a demanding computational challenge, due to the intensive calculations required to produce all-sky maps with fine angular resolution including high-order scattering effects. We present in this letter a physically consistent, semi-analytic two-parameter model of the all-sky radiance produced by an artificial light source that encodes efficiently the spectral radiance in all directions of the sky above the observer. The two parameters of this function are derived from the state of the atmosphere, the distance to the observer, and the source’s angular and spectral emission pattern. The anthropogenic all-sky radiance at any place on Earth can be easily calculated by adding up the contributions of the surrounding artificial sources, using the information available from nighttime satellite imagery and ground-truth lighting inventories. This opens the way for the elaboration of a global world map of the artificial all-sky brightness.

Daytime all-sky radiance distribution pattern for near-earth space SWIR stellar navigation

Short-Wavelength-Infrared (SWIR) stellar navigation technology is a focused area of frontier research in near-earth space. However, in the process of star trackers' optical scheme demonstration, imaging link construction, and algorithm test optimization, large-scale sky background data in JHKs bands with different atmosphere modes, aerosol types, earth-surface types, and sun positions are required as inputs. Although the existing atmospheric radiative transfer code can generate extensive uncontinuous background radiation data, some inherent defects, including high computational complexity, low work efficiency, and long operation time, severely restrict the application in engineering. For the purpose of making sky background radiation data far more productively in action, it is essential to establish the distribution model to describe the characteristics of sky background radiation. Therefore, the daytime all-sky radiance distribution pattern for JHKs bands is built based on the Hosek model, and the Adaptive Moment Estimation (Adam) method is applied to solve and optimize the distribution parameters. The correctness of the distribution pattern is verified based on the measurement data. The results show that the daytime all-sky radiance distribution pattern is constructed exactly and can provide a firm reference for SWIR star trackers' system design and performance evaluation.

Impact of TROPICS Radiances on Tropical Cyclone Prediction in an OSSE

AbstractAs part of the NASA Earth Venture-Instrument program, the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission, to be launched in January 2022, will deliver unprecedented rapid-update microwave measurements over the tropics that can be used to observe the evolution of the precipitation and thermodynamic structure of tropical cyclones (TCs) at meso- and synoptic scales. TROPICS consists of six CubeSats, each hosting a passive microwave radiometer that provides radiance observations sensitive to atmospheric temperature, water vapor, precipitation, and precipitation-sized ice particles. In this study, the impact of TROPICS all-sky radiances on TC analyses and forecasts is explored through a regional mesoscale observing system simulation experiment (OSSE). The results indicate that the TROPICS all-sky radiances can have positive impacts on TC track and intensity forecasts, particularly when some hydrometeor state variables and other state variables of the data assimilation system that are relevant to cloudy radiance assimilation are updated. The largest impact on the model analyses is seen in the humidity fields, regardless of whether or not there are radiances assimilated from other satellites. TROPICS radiances demonstrate large impact on TC analyses and forecasts when other satellite radiances are absent. The assimilation of the all-sky TROPICS radiances without default radiances leads to a consistent improvement in the low- and midtropospheric temperature and wind forecasts throughout the 5-day forecasts, but only up to 36-h lead time in the humidity forecasts at all pressure levels. This study illustrates the potential benefits of TROPICS data assimilation for TC forecasts and provides a potentially streamlined pathway for transitioning TROPICS data from research to operations postlaunch.

Data assimilation challenges posed by nonlinear operators: A comparative study of ensemble and variational filters and smoothers

AbstractThe ensemble Kalman Filter (EnKF) and the 4D variational method (4DVar) are the most commonly used filters and smoothers in atmospheric science. These methods typically approximate prior densities using a Gaussian and solve a linear system of equations for the posterior mean and covariance. Therefore, strongly nonlinear model dynamics and measurement operators can lead to bias in posterior estimates. To improve the performance in nonlinear regimes, minimization of the 4DVar cost function typically follows multiple sets of iterations, known as an “outer loop”, which helps reduce bias caused by linear assumptions. Alternatively, “iterative ensemble methods” follow a similar strategy of periodically re-linearizing model and measurement operators. These methods come with different, possibly more appropriate, assumptions for drawing samples from the posterior density, but have seen little attention in numerical weather prediction (NWP) communities. Lastly, particle filters (PFs) present a purely Bayesian filtering approach for state estimation, which avoids many of the assumptions made by the above methods. Several strategies for applying localized PFs for NWP have been proposed very recently. The current study investigates intrinsic limitations of current data assimilation methodology for applications that require nonlinear measurement operators. In doing so, it targets a specific problem that is relevant to the assimilation of remotely-sensed measurements, such as radar reflectivity and all-sky radiances, which pose challenges for Gaussian-based data assimilation systems. This comparison includes multiple data assimilation approaches designed recently for nonlinear/non-Gaussian applications, as well as those currently used for NWP.

Exploring the Assimilation of ATMS Cloud Retrieval Products and its Benefits for CrIS All-sky Radiance Simulations

AbstractAimed at improving all-sky Cross-track Infrared Sounder (CrIS) radiance assimilation, this study explores the benefits for CrIS all-sky radiance simulations, focusing on the accuracy of background cloud information, through assimilating cloud liquid water path (LWP), ice water path (IWP), and rain water path (RWP) data retrieved from the Advanced Technology Microwave Sounder (ATMS). The Community Radiative Transfer Model (CRTM), which considers cloud scattering and absorption processes, is applied to simulate CrIS radiances. The Gridpoint Statistical Interpolation ensemble-variational data assimilation (DA) is updated by incorporating ensemble covariances of hydrometeor variables and observation operators of LWP, IWP, and RWP. First, two DA experiments named DActrl and DAcwp are conducted with (DAcwp) and without (DActrl) assimilating ATMS LWP, IWP, and RWP data. Assimilating ATMS cloud retrieval data results in better spatial distributions of hydrometers for both a Meiyu rainfall case and a typhoon case. Analyses of DActrl and DAcwp are then used as input to the CRTM to generate CrIS all-sky radiance simulations SMallsky_DActrl and SMallsky_DAcwp, respectively. Improvements in the DAcwp analyses of hydrometeor variables are found to benefit CrIS radiance simulations, especially in cloudy regions. A long period of statistics reveals that the biases and standard deviations of all-sky observations minus simulations from SMallsky_DAcwp are notably smaller than those from SMallsky_DActrl. This pilot study suggests the potential benefit of combining the use of microwave cloud retrieval products for all-sky infrared DA.

Benefits of the Advanced Baseline Imager (ABI) for Ensemble-Based Analysis and Prediction of Severe Thunderstorms

AbstractRecent studies have demonstrated advances in the analysis and prediction of severe thunderstorms and other weather hazards by assimilating infrared (IR) all-sky radiances into numerical weather prediction models using advanced ensemble-based techniques. It remains an open question how many of these advances are due to improvements in the radiance observations themselves, especially when compared with radiance observations from preceding satellite imagers. This study investigates the improvements gained by assimilation of IR all-sky radiances from the Advanced Baseline Imager (ABI) on board GOES-16 compared to those from its predecessor imager. Results show that all aspects of the improvements in ABI compared with its predecessor imager—finer spatial resolution, shorter scanning intervals, and more channels covering a wider range of the spectrum—contribute to more accurate ensemble analyses and forecasts of the targeted severe thunderstorm event, but in different ways. The clear-sky regions within the assimilated all-sky radiance fields have a particularly beneficial influence on the moisture fields. Results also show that assimilating different IR channels can lead to oppositely signed increments in the moisture fields, a by-product of inaccurate covariances at large distances resulting from sampling errors. These findings pose both challenges and opportunities in identifying appropriate vertical localizations and IR channel combinations to produce the best possible analyses in support of severe weather forecasting.

Nonlinear Forecast Error Growth of Rapidly Intensifying Hurricane Harvey (2017) Examined through Convection-Permitting Ensemble Assimilation of GOES-16 All-Sky Radiances

AbstractThe dynamics and predictability of the rapid intensification (RI) of Hurricane Harvey (2017) were examined using convection-permitting initialization, analysis, and prediction from a cycling ensemble Kalman filter (EnKF) that assimilated all-sky infrared radiances from the Advanced Baseline Imager on GOES-16. The EnKF analyses were able to evolve the various scales of the radiance fields associated with Harvey close to those observed, including those associated with scattered individual convective cells before the onset of rapid intensification (RI) and the organized vortex-scale convective system during and after RI. This was true for more than 3 days of a continuous assimilation cycling. Deterministic forecasts initialized from the EnKF analyses captured the rapidly deepening intensity of Harvey more than 24 h prior to its onset. To explore the predictability of Harvey’s intensity during RI, ensemble probabilistic forecasts and sensitivity analyses were conducted. It was found that significant ensemble spread growth was induced by initial perturbations individually in either the wind or moisture fields. The nonlinear interactions between wind and moisture perturbations further limited the predictability of the intensification process of Harvey by increasing the uncertainty in the simulated wind and moisture distributions and modifying the convective activity and its feedback on vortex flow. This study highlights both the importance of better initializing the dynamic and moisture state variables simultaneously and the potential contribution of satellite all-sky radiance assimilation on constraining them and their associated convective activity that impacts RI of tropical cyclones.