Assimilation of blended in situ-satellite snow water equivalent into the National Water Model for improving hydrologic simulation in two US river basins

This study investigates the potential of assimilating a 1/8° blended in situ-satellite snow water equivalent (SWE) product for improving snow and streamflow predictions of the National Water Model (NWM). The blended product is assimilated into the NWM via a three-dimensional variational (3DVAR) scheme and a direct insertion (DI) scheme, with a daily (1d) and a every 5 days (5d) assimilation frequencies. The experiments are for the Upper Colorado River Basin (UCRB) and Susquehanna River Basin (SRB), which feature seasonal and ephemeral snow covers, respectively. Results indicate that 3DVAR with a 5d assimilation frequency generally outperforms the other scenarios. The assimilation of the blended SWE product mitigates the underestimation of SWE evident in the open-loop simulations for both basins and its impacts are more pronounced for UCRB than for SRB since snowfall is the main source of precipitation in the former. Assimilation leads to improved streamflow over a majority of SRB subbasins, but over a minority of UCRB subbasins. The degradations in streamflow for UCRB subbasins are mainly caused by the overestimated SWE. In addition, the open-loop simulation often produces an earlier streamflow peak in UCRB, and this error is mitigated to a limited extent by assimilation. These findings in aggregate suggest that the efficacy of snow assimilation is strongly dependent upon the types of snowpack and differential assimilation methods and frequencies.

Intercomparison of Resampling Algorithms for Advanced Technology Microwave Sounder (ATMS)

The observations from satellite microwave-sounding instruments have been widely used in weather and climate studies. Since the data resolution varies with frequency and satellite viewing angle, it is normally required that the measurements at each frequency be resampled to obtain a uniform resolution prior to various applications. In this study, the ATOVS and AVHRR pre-processing package (AAPP) Fourier transform algorithm is modified for ATMS data and the results are compared with those derived from Backus–Gilbert inversion (BGI) and the original AAPP. From the simulated and observed ATMS data, we demonstrated the new algorithm has better results in terms of imaging quality and noise suppression, compared with BGI and AAPP. In general, our modified AAPP algorithm reduces the error by at least about 0.5 K in ATMS channels 2 and 6 and at all the viewing angles.

Preliminary Evaluation of FY-3E Microwave Temperature Sounder Performance Based on Observation Minus Simulation

The FY-3E satellite was successfully launched on 5 July 2021 and carries on board the Microwave Temperature Sounder-Ⅲ (MWTS-Ⅲ). In this study, the biases of MWTS-Ⅲ data with respect to simulations are analyzed according to the instrument field of view and location latitude over the Pacific region. The cloud liquid water path (CLWP) over oceans is retrieved from two new window channels at 23.8 and 31.4 GHz and is used for detecting the clouds-affected microwave sounding data. The absolute bias between the observed and simulated brightness temperature (O–B) under the clear sky point is, in general, less than 2.0 K, depending on the MWTS-III channel. The standard deviations of O-B in most channels are less 1.0 K, but they are 1–1.5 K in channels 1–4 and 17. The average and the standard deviation of O−B from the channels 1–10 shows an obvious symmetrical variation with FOV. The evaluation results all indicate good prospects for the assimilation application of FY-3E microwave sounding data.

Comparison of the Potential Impact to the Prediction of Typhoons of Various Microwave Sounders Onboard a Geostationary Satellite

A microwave radiometer onboard a geostationary satellite can provide for the continuous atmospheric sounding of rapidly evolving convective events even in the presence of clouds, which has aroused great research interest in recent decades. To approach the problem of high-spatial resolution and large-size antennas, three promising geostationary microwave (GEO-MW) solutions—geostationary microwave radiometer (GMR) with a 5 m real aperture antenna, geostationary synthetic thinned aperture radiometer (GeoSTAR) with a Y-shaped synthetic aperture array, and geostationary interferometric microwave sounder (GIMS) with a rotating circular synthetic aperture array—have been proposed. To compare the potential impact of assimilating the three GEO-MW sounders to typhoon prediction, observing system simulation experiments (OSSEs) with the simulated 50–60 GHz observing brightness temperature data were conducted using the mesoscale numerical model Weather Research and Forecasting (WRF) and WRF Date Assimilation-Four dimensional variational (WRFDA-4Dvar) assimilation system for Typhoons Hagibis and Bualoi which occurred in 2019. The results show that the assimilation of the three GEO-MW instruments with 4 channels of data at 50–60 GHz could lead to general positive impacts in this study. Compared with the control experiment, for the two cases of Bualoi and Hagibis, GMR improves the average 72 h typhoon track forecast accuracy by 24% and 43%, GeoSTAR by 33% and 50%, and GIMS by 10% and 29%, respectively. Overall, the three GEO-MW instruments show considerable promise in atmospheric sounding and data assimilation. The difference among these positive impacts seems to depend on the observation error of the three potential instruments. GeoSTAR is slightly better than the other two GEO-MW sounders, which may be because it has the smallest observation error of the 4 assimilation channels. Generally, this study illustrates that the performance of these three GEO-MW sounders is potentially adequate to support assimilation into numerical weather prediction models for typhoon prediction.

A Machine Learning Snowfall Retrieval Algorithm for ATMS

This article describes the development of a machine learning (ML)-based algorithm for snowfall retrieval (Snow retrievaL ALgorithm fOr gpM–Cross Track, SLALOM-CT), exploiting ATMS radiometer measurements and using the CloudSat CPR snowfall products as references. During a preliminary analysis, different ML techniques (tree-based algorithms, shallow and convolutional neural networks—NNs) were intercompared. A large dataset (three years) of coincident observations from CPR and ATMS was used for training and testing the different techniques. The SLALOM-CT algorithm is based on four independent modules for the detection of snowfall and supercooled droplets, and for the estimation of snow water path and snowfall rate. Each module was designed by choosing the best-performing ML approach through model selection and optimization. While a convolutional NN was the most accurate for the snowfall detection module, a shallow NN was selected for all other modules. SLALOM-CT showed a high degree of consistency with CPR. Moreover, the results were almost independent of the background surface categorization and the observation angle. The reliability of the SLALOM-CT estimates was also highlighted by the good results obtained from a direct comparison with a reference algorithm (GPROF).

Consistency and Stability of SNPP ATMS Microwave Observations and COSMIC-2 Radio Occultation over Oceans

Radio occultation (RO) sensor measurements have critical roles in numerical weather prediction (NWP) by complementing microwave and infrared sounder measurements with information of the atmospheric profiles at high accuracy, precision, and vertical resolution. This study evaluates Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2) wet temperature and humidity data products’ consistency and stability through inter-comparison with SNPP advanced technology microwave sounder (ATMS) measurements. Through the community radiative transfer model (CRTM), brightness temperature (BT) at SNPP ATMS channels are simulated with COSMIC-2 retrieved atmospheric profiles from two versions of the University Corporation for Atmospheric Research (UCAR) wet profiles (WETprf and WETpf2) as inputs to the CRTM simulation. The analysis was focused on ATMS sounding channels CH07–14 and CH19–22 with sounding weighting function peak heights from 3.2 to 35 km. The COSMIC-2 vs. ATMS inter-comparison indicates that their BT biases are consistent, and the latitudinal difference is <0.3 K over three latitudinal regions. The differences between the two versions of UCAR COSMIC-2 wet profiles are identified and attributed to the differences in the implementation of 1DVAR retrieval algorithms. The stability between UCAR near real-time COSMIC-2 wet profile data and ATMS measurements is also well-maintained. It is demonstrated that the well-sustained quality of COSMIC-2 RO data makes itself a well-suited reference sensor to capture the calibration update of SNPP ATMS. Furthermore, the impacts of the assimilation of COSMIC-2 data into the European Centre for Medium-Range Weather Forecasts (ECMWF) model after 25 March 2020, are evaluated by trending observation-minus-background (O-B) biases, which confirms the statistically significant positive impacts of COSMIC-2 on the ECMWF reanalysis. The validation of stability and consistency between COSMIC-2 and SNPP ATMS ensures the quality of RO and microwave sounder data assimilated into the NWP models.

Applications of the Advanced Radiative Transfer Modeling System (ARMS) to Characterize the Performance of Fengyun–4A/AGRI

This study applies the Advanced Radiative Transfer Modeling System (ARMS), which was developed to accelerate the uses of Fengyun satellite data in weather, climate, and environmental applications in China, to characterize the biases of seven infrared (IR) bands of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Chinese geostationary meteorological satellite, Fengyun–4A. The AGRI data are quality controlled to eliminate the observations affected by clouds and contaminated by stray lights during the mid–night from 1600 to 1800 UTC during spring and autumn. The mean biases, computed from AGRI IR observations and ARMS simulations from the National Center for Environmental Prediction (NCEP) Final analysis data (FNL) as input, are within −0.7–1.1 K (0.12–0.75 K) for all seven IR bands over the oceans (land) under clear–sky conditions. The biases show seasonal variation in spatial distributions at bands 11–13, as well as a strong dependence on scene temperatures at bands 8–14 and on satellite zenith angles at absorption bands 9, 10, and 14. The discrepancies between biases estimated using FNL and the European Center for Medium–Range Weather Forecasts Reanalysis–5 (ERA5) are also discussed. The biases from water vapor absorption bands 9 and 10, estimated using ERA5 over ocean, are smaller than those from FNL. Such discrepancies arise from the fact that the FNL data are colder (wetter) than the ERA5 in the middle troposphere (upper–troposphere).

Application of the Deep Neural Network in Retrieving the Atmospheric Temperature and Humidity Profiles from the Microwave Humidity and Temperature Sounder Onboard the Feng-Yun-3 Satellite

The shallow neural network (SNN) is a popular algorithm in atmospheric parameters retrieval from microwave remote sensing. However, the deep neural network (DNN) has a stronger nonlinear mapping capability compared to SNN and has great potential for applications in microwave remote sensing. The Microwave Humidity and Temperature Sounder (Beijing, China, MWHTS) onboard the Fengyun-3 (FY-3) satellite has the ability to independently retrieve atmospheric temperature and humidity profiles. A study on the application of DNN in retrieving atmospheric temperature and humidity profiles from MWHTS was carried out. Three retrieval schemes of atmospheric parameters in microwave remote sensing based on DNN were performed in the study of bias correction of MWHTS observation and the retrieval of the atmospheric temperature and humidity profiles using MWHTS observations. The experimental results show that, compared with SNN, DNN can obtain better bias-correction results when applied to MWHTS observation, and can obtain higher retrieval accuracy of temperature and humidity profiles in all three retrieval schemes. Meanwhile, DNN shows higher stability than SNN when applied to the retrieval of temperature and humidity profiles. The comparative study of DNN and SNN applied in different atmospheric parameter retrieval schemes shows that DNN has a more superior performance.

Tropical Cyclone Center Positioning Using Single Channel Microwave Satellite Observations of Brightness Temperature

Satellite observations of brightness temperature from the Advanced Technology Microwave Sounder (ATMS) and Microwave Humidity Sounder (MHS) humidity sounding channels can provide relatively high horizontal resolution information about cloud and atmospheric moisture in the troposphere, thus revealing the structures of tropical cyclones (TCs). There is usually a high brightness temperature in a TC eye region and low brightness temperature reflecting spiral rain bands. An azimuthal spectral analysis method is used as a center-fixing algorithm to determine the TC center objectively using the brightness temperature observations of the ATMS humidity-sounding channel 18 (183.31 ± 7.0 GHz) and MHS humidity-sounding channel 5 (190.31 GHz). The position in the brightness temperature field encompassing a TC that achieves the largest symmetric component is regarded as the TC center. Two Atlantic hurricanes in 2012, Hurricanes Sandy and Isaac, are first used to analyze the performance of the TC center-fixing technique. Compared with the National Hurricane Center best track, the root-mean-square differences of the center fixing results for Hurricanes Sandy and Isaac are less than 47.3 and 34.3 km, respectively. It is found that the uncertainty of the TC center-fixing algorithm and thus the difference from the best track increases when the brightness temperature distribution within a TC is significantly asymmetric. Then, the TC center-fixing technique is validated for all tropical storms and hurricanes over Northern Atlantic and Western Pacific in 2019. Compared with the best track data, the root-mean-square differences for tropical storms and hurricanes are 33.81 and 26.20 km, respectively. The demonstrated successful performance of the proposed TC center-fixing algorithm to use the single channel of microwave humidity sounders for TC positioning is important for vortex initialization in operational hurricane forecasts.

Impact of the One-Stream Cloud Detection Method on the Assimilation of AMSU-A Data in GRAPES

Clouds affect the assimilation of microwave data from satellites and therefore the detection of clouds is important under both clear sky and cloudy conditions. We introduce a new cloud detection method based on the assimilation of data from the advanced microwave sounder unit A (AMSU-A) and the microwave humidity sounder (MHS) into the global and regional assimilation and prediction system (GRAPES) and use forecast experiments to evaluate its impact. The new cloud detection method can retain more observational data than the current method in GRAPES, thereby improving the assimilation of AMSU-A data. Verification of the method showed that, by improving the forecast of the lower-level air temperature and geopotential height, the new cloud detection method improved the forecast of the track of two typhoons. The forecast of a large-scale weather system in GRAPES was also improved by the new method in the later period of the forecast.

Validation of Carbon Trace Gas Profile Retrievals from the NOAA-Unique Combined Atmospheric Processing System for the Cross-Track Infrared Sounder

This paper provides an overview of the validation of National Oceanic and Atmospheric Administration (NOAA) operational retrievals of atmospheric carbon trace gas profiles, specifically carbon monoxide (CO), methane (CH4) and carbon dioxide (CO2), from the NOAA-Unique Combined Atmospheric Processing System (NUCAPS), a NOAA enterprise algorithm that retrieves atmospheric profile environmental data records (EDRs) under global non-precipitating (clear to partly cloudy) conditions. Vertical information about atmospheric trace gases is obtained from the Cross-track Infrared Sounder (CrIS), an infrared Fourier transform spectrometer that measures high resolution Earth radiance spectra from NOAA operational low earth orbit (LEO) satellites, including the Suomi National Polar-orbiting Partnership (SNPP) and follow-on Joint Polar Satellite System (JPSS) series beginning with NOAA-20. The NUCAPS CO, CH4, and CO2 profile EDRs are rigorously validated in this paper using well-established independent truth datasets, namely total column data from ground-based Total Carbon Column Observing Network (TCCON) sites, and in situ vertical profile data obtained from aircraft and balloon platforms via the NASA Atmospheric Tomography (ATom) mission and NOAA AirCore sampler, respectively. Statistical analyses using these datasets demonstrate that the NUCAPS carbon gas profile EDRs generally meet JPSS Level 1 global performance requirements, with the absolute accuracy and precision of CO 5% and 15%, respectively, in layers where CrIS has vertical sensitivity; CH4 and CO2 product accuracies are both found to be within ±1%, with precisions of ≈1.5% and ⪅0.5%, respectively, throughout the tropospheric column.

Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS)

We present the development of a dynamic over-ocean radiometric bias correction for the Microwave Integrated Retrieval System (MiRS) which accounts for spatial, temporal, spectral, and angular dependence of the systematic differences between observed and forward model-simulated radiances. The dynamic bias correction, which utilizes a deep neural network approach, is designed to incorporate dependence on the atmospheric and surface conditions that impact forward model biases. The approach utilizes collocations of observed Suomi National Polar-orbiting Partnership/Advanced Technology Microwave Sounder (SNPP/ATMS) radiances and European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses which are used as input to the Community Radiative Transfer Model (CRTM) forward model to develop training data of radiometric biases. Analysis of the neural network performance indicates that in many channels, the dynamic bias is able to reproduce realistically both the spatial patterns of the original bias and its probability distribution function. Furthermore, retrieval impact experiments on independent data show that, compared with the baseline static bias correction, using the dynamic bias correction can improve temperature and water vapor profile retrievals, particularly in regions with higher Cloud Liquid Water (CLW) amounts. Ocean surface emissivity retrievals are also improved, for example at 23.8 GHz, showing an increase in correlation from 0.59 to 0.67 and a reduction of standard deviation from 0.035 to 0.026.

NOAA Operational Microwave Sounding Radiometer Data Quality Monitoring and Anomaly Assessment Using COSMIC GNSS Radio-Occultation Soundings

National Oceanic and Atmospheric Administration (NOAA) operational Advanced Technology Microwave Sounder (ATMS) and Advanced Microwave Sounding Unit-A (AMSU-A) data used in numerical weather prediction and climate analysis are essential to protect life and property and maintain safe and efficient commerce. Routine data quality monitoring and anomaly assessment is important to sustain data effectiveness. One valuable parameter used to monitor microwave sounder data quality is the antenna temperature (Ta) difference (O-B) computed between direct instrument Ta measurements and forward radiative transfer model (RTM) brightness temperature (Tb) simulations. This requires microwave radiometer data to be collocated with atmospheric temperature and moisture sounding profiles, so that representative boundary conditions are used to produce the RTM-simulated Tb values. In this study, Constellation Observing System for Meteorology, Ionosphere, and Climate/Formosa Satellite Mission 3 (COSMIC) Global Navigation Satellite System (GNSS) Radio Occultation (RO) soundings over the ocean and equatorward of 60° latitude are used as input to the Community RTM (CRTM) to generate simulated NOAA-18, NOAA-19, Metop-A, and Metop-B AMSU-A and S-NPP and NOAA-20 ATMS Tb values. These simulated Tb values, together with observed Ta values that are nearly simultaneous in space and time, are used to compute Ta O-B statistics on monthly time scales for each instrument. In addition, the CRTM-simulated Tb values based on the COSMIC GNSS RO soundings can be used as a transfer standard to inter-compare Ta values from different microwave radiometer makes and models that have the same bands. For example, monthly Ta O-B statistics for NOAA-18 AMSU-A Channels 4–12 and NOAA-20 ATMS Channels 5–13 can be differenced to estimate the “double-difference” Ta biases between these two instruments for the corresponding frequency bands. This study reveals that the GNSS RO soundings are critical to monitoring and trending individual instrument O-B Ta biases and inter-instrument “double-difference” Ta biases and also to estimate impacts of some sensor anomalies on instrument Ta values.

Comparison of the Remapping Algorithms for the Advanced Technology Microwave Sounder (ATMS)

One of the limitations in using spaceborne, microwave radiometer data for atmospheric remote sensing is the nonuniform spatial resolution. Remapping algorithms can be applied to the data to ameliorate this limitation. In this paper, two remapping algorithms, the Backus–Gilbert inversion (BGI) technique and the filter algorithm (AFA), widely used in the operational data preprocessing of the Advanced Technology Microwave Sounder (ATMS), are investigated. The algorithms are compared using simulations and actual ATMS data. Results show that both algorithms can effectively enhance or degrade the resolution of the data. The BGI has a higher remapping accuracy than the AFA. It outperforms the AFA by producing less bias around coastlines and hurricane centers where the signal changes sharply. It shows no obvious bias around the scan ends where the AFA has a noticeable positive bias in the resolution-enhanced image. However, the BGI achieves the resolution enhancement at the expense of increasing the noise by 0.5 K. The use of the antenna pattern instead of the point spread function in the algorithm causes the persistent bias found in the AFA-remapped image, leading not only to an inaccurate antenna temperature expression but also to the neglect of the geometric deformation of the along-scan field-of-views.

Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation

Accurate precipitation detection is one of the most important factors in satellite data assimilation, due to the large uncertainties associated with precipitation properties in radiative transfer models and numerical weather prediction (NWP) models. In this paper, a method to achieve remote sensing of precipitation and classify its intensity over land using a co-located ground-based radar network is described. This method is intended to characterize the O−B biases for the microwave humidity sounder -2 (MWHS-2) under four categories of precipitation: precipitation-free (0–5 dBZ), light precipitation (5–20 dBZ), moderate precipitation (20–35 dBZ), and intense precipitation (>35 dBZ). Additionally, O represents the observed brightness temperature (TB) of the satellite and B is the simulated TB from the model background field using the radiative transfer model. Thresholds for the brightness temperature differences between channels, as well as the order relation between the differences, exhibited a good estimation of precipitation. It is demonstrated that differences between observations and simulations were predominantly due to the cases in which radar reflectivity was above 15 dBZ. For most channels, the biases and standard deviations of O−B increased with precipitation intensity. Specifically, it is noted that for channel 11 (183.31 ± 1 GHz), the standard deviations of O−B under moderate and intense precipitation were even smaller than those under light precipitation and precipitation-free conditions. Likewise, abnormal results can also be seen for channel 4 (118.75 ± 0.3 GHz).

Combining Water Fraction and DEM-Based Methods to Create a Coastal Flood Map: A Case Study of Hurricane Harvey

Tropical cyclones are incredibly destructive and deadly, inflicting immense losses to coastal properties and infrastructure. Hurricane-induced coastal floods are often the biggest threat to life and the coastal environment. A quick and accurate estimation of coastal flood extent is urgently required for disaster rescue and emergency response. In this study, a combined Digital Elevation Model (DEM) based water fraction (DWF) method was implemented to simulate coastal floods during Hurricane Harvey on the South Texas coast. Water fraction values were calculated to create a 15 km flood map from multiple channels of the Advanced Technology Microwave Sound dataset. Based on hydrological inundation mechanism and topographic information, the coarse-resolution flood map derived from water fraction values was then downscaled to a high spatial resolution of 10 m. To evaluate the DWF result, Storm Surge Hindcast product and flood-reported high-water-mark observations were used. The results indicated a high overlapping area between the DWF map and buffered flood-reported high-water-marks (HWMs), with a percentage of more than 85%. Furthermore, the correlation coefficient between the DWF map and CERA SSH product was 0.91, which demonstrates a strong linear relationship between these two maps. The DWF model has a promising capacity to create high-resolution flood maps over large areas that can aid in emergency response. The result generated here can also be useful for flood risk management, especially through risk communication.

Atmospheric Retrievals and Assessment for Microwave Observations from Chinese FY-3C Satellite during Hurricane Matthew

The evolution process of hurricane Matthew (NO. 8, 2016) was simulated using the mesoscale Weather Research and Forecasting (WRF) model at temporal resolution of 5 min and spatial resolution of 15 km. The atmospheric temperature and humidity profiles were retrieved accordingly for diagnostic analysis of the short-term heavy rainstorm. The satellite-based microwave observations from Microwave Humidity and Temperature Sounder (MWHTS) instrument on board the FY-3C polar-orbiting satellite were matched with the WRF grid points. In particular, the in-orbit calibration and data quality control are detailed, and an innovative method combining artificial neural network (ANN) and 1-D variational approach is presented to derive the high-performance retrieval profiles. Results show that the root-mean-square errors of the retrieved temperature and water vapor density profiles are 0.75 K and 0.41 g/m3, respectively. In addition, this study used both the retrievals and radiance from MWHTS as input to the WRF Data Assimilation (WRFDA) model to forecast the track and intensity of hurricane Matthew. The forecast results were cross-compared with the best track to verify the radiance quality and performance of the retrievals, especially for the 118 GHz channel, which was firstly used in meteorological satellite.

An Overview of the Science Performances and Calibration/Validation of Joint Polar Satellite System Operational Products

The Suomi National Polar-orbiting Partnership (S-NPP) satellite, launched in October 2011, initiated a series of the next-generation weather satellites for the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) program. The JPSS program at the Center for Satellite Applications and Research (JSTAR) leads the development of the algorithms, the calibration and validation of the products to meet the specified requirements, and long-term science performance monitoring and maintenance. All of the S-NPP products have been validated and are in successful operation. The recently launched JPSS-1 (renamed as NOAA-20) satellite is producing high-quality data products that have been available from S-NPP, along with additional products, as a direct result of the instrument upgrades and science improvements. This paper presents an overview of the JPSS product suite, the performance metrics achieved for the S-NPP, and the utilization of the products by NOAA stakeholders and user agencies worldwide. The status of NOAA-20 science data products and ongoing calibration/validation (Cal/Val) efforts are discussed for user awareness. In addition, operational implementation statuses of JPSS enterprise (multisensor and multiplatform) science algorithms for product generation and science product reprocessing efforts for the S-NPP mission are discussed.