Comparative Study of the 60 GHz and 118 GHz Oxygen Absorption Bands for Sounding Sea Surface Barometric Pressure

The 60 GHz and 118 GHz oxygen absorption bands are prominent in the passive microwave remote sensing of atmospheric temperature, and also can be used for sounding sea surface barometric pressure (SSP). Microwave Temperature Sounder II (MWTS-II) has 13 channels in the 60 GHz band, and Microwave Humidity and Temperature Sounder (MWHTS) has 8 channels in the 118 GHz band. They are both carried on Fengyun-3C Satellite (FY-3C) and Fengyun-3D Satellite (FY-3D), which provide measurements for comparing the retrieval accuracies of SSP using 60 GHz and 118 GHz bands. In this study, based on the weighting functions for MWHTS and MWTS-II, the 60 GHz and 118 GHz channel combinations representing 60 GHz and 118 GHz are established, respectively, and the retrieval accuracies of SSP from these two channel combinations are compared in different weather conditions. The experimental results show that the retrieval accuracy of SSP at 60 GHz is higher than that of 118 GHz in clear, cloudy, and rainy sky conditions. In addition, the retrieval experiments of SSP from MWTS-II and MWHTS are also carried out, and the experimental results show that the retrieval accuracy of SSP from MWTS-II is higher. The comparative study of the 60 GHz and 118 GHz for sounding SSP can provide support for the theoretical study of microwave remote sensing of SSP with practical measurements, and further contribute to understand the performance of 60 GHz and 118 GHz in atmospheric sounding.

The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

The Potential Impact of Assimilating Synthetic Microwave Radiances Onboard a Future Geostationary Satellite on the Prediction of Typhoon Lekima Using the WRF Model

Geostationary meteorological satellites can provide continuous observations of high-impact weather events with a high temporal and spatial resolution. Sounding the atmosphere using a microwave instrument onboard a geostationary satellite has aroused great study interests for years, as it would increase the observational efficiency as well as provide a new perspective in the microwave spectrum to the measuring capability for the current observational system. In this study, the capability of assimilating future geostationary microwave sounder (GEOMS) radiances was developed in the Weather Research and Forecasting (WRF) model’s data assimilation (WRFDA) system. To investigate if these frequently updated and widely distributed microwave radiances would be beneficial for typhoon prediction, observational system simulation experiments (OSSEs) using synthetic microwave radiances were conducted using the mesoscale numerical model WRF and the advanced hybrid ensemble–variational data assimilation method for the Lekima typhoon that occurred in early August 2019. The results show that general positive forecast impacts were achieved in the OSSEs due to the assimilation of GEOMS radiances: errors of analyses and forecasts in terms of wind, humidity, and temperature were both reduced after assimilating GEOMS radiances when verified against ERA-5 data. The track and intensity predictions of Lekima were also improved before 68 h compared to the best track data in this study. In addition, rainfall forecast improvements were also found due to the assimilation impact of GEOMS radiances. In general, microwave observations from geostationary satellites provide the possibility of frequently assimilating wide-ranging microwave information into a regional model in a finer resolution, which can potentially help improve numerical weather prediction (NWP).

Hailstorm Detection by Satellite Microwave Radiometers

Passive microwave measurements from satellites have been used to identify the signature of hail in intense thunderstorms. The scattering signal of hailstones is typically observed as a strong depression of upwelling brightness temperatures from the cloud to the satellite. Although the relation between scattering signal and hail diameter is often assumed linear, in this work a logistic model is used which seems to well approximate the complexity of the radiation extinction process by varying the hail cross-section. A novel probability-based method for hail detection originally conceived for AMSU-B/MHS and now extended to ATMS, GMI, and SSMIS, is presented. The measurements of AMSU-B/MHS were analyzed during selected hailstorms over Europe, South America and the US to quantify the extinction of radiation due to the hailstones and large ice aggregates. To this aim, a probabilistic growth model has been developed. The validation analysis based on 12-year surface hail observations over the US (NOAA official reports) collocated with AMSU-B overpasses have demonstrated the high performance of the hail detection method in distinguishing between moderate and severe hailstorms, fitting the seasonality of hail patterns. The flexibility of the method allowed its experimental application to other microwave radiometers equipped with MHS-like frequency channels revealing a high level of portability.

The New Potential of Deep Convective Clouds as a Calibration Target for a Geostationary UV/VIS Hyperspectral Spectrometer

As one of geostationary earth orbit constellation for environmental monitoring over the next decade, the Geostationary Environment Monitoring Spectrometer (GEMS) has been designed to observe the Asia-Pacific region to provide information on atmospheric chemicals, aerosols, and cloud properties. In order to continuously monitor sensor performance after its launch in early 2020, we suggest in this paper deep convective clouds (DCCs) as a possible target for the vicarious calibration of the GEMS, the first ultraviolet and visible hyperspectral sensor onboard a geostationary satellite. The Tropospheric Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI) are used as a proxy for GEMS, and a conventional DCC-detection approach applying a thermal threshold test is used for DCC detection based on collocations with the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. DCCs are frequently detected over the GEMS observation area at an average of over 200 pixels within a single observation scene. Considering the spatial resolution of the GEMS (3.5 × 8 km2), which is similar to the TROPOMI and its temporal resolution (eight times a day), the availability of DCCs is expected to be sufficient for the vicarious calibration of the GEMS. Inspection of the DCC reflectivity spectra estimated from OMI and TROPOMI data also shows promising results. The estimated DCC spectra are in good agreement within a known uncertainty range with comparable spectral features even with different observation geometries and sensor characteristics. When DCC detection is improved further by applying both visible and infrared tests, the variability of DCC reflectivity from TROPOMI data is reduced from 10% to 5%. This is mainly due to the efficient screening out of cold, thin cirrus clouds in the visible test and of bright, warm clouds in the infrared test. Precise DCC detection is also expected to contribute to the accurate characterization of cloud reflectivity, which will be investigated further in future research.

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).

Satellites See the World’s Atmosphere

Satellite meteorology is a relatively new branch of the atmospheric sciences. The field emerged in the late 1950s during the Cold War and built on the advances in rocketry after World War II. In less than 70 years, satellite observations have transformed the way scientists observe and study Earth. This paper discusses some of the key advances in our understanding of the energy and water cycles, weather forecasting, and atmospheric composition enabled by satellite observations. While progress truly has been an international achievement, in accord with a monograph observing the centennial of the American Meteorological Society, as well as limited space, the emphasis of this chapter is on the U.S. satellite effort.

The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer

This paper describes a new rainfall rate retrieval algorithm, developed within the EUMETSAT H SAF program, based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR v3), designed to work with the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI). A new rain/no-rain classification scheme, also based on the NN approach, which provides different rainfall masks for different minimum thresholds and degree of reliability, is also described. The algorithm is trained on an extremely large observational database, built from GPM global observations between 2014 and 2016, where the NASA 2B-CMB (V04) rainfall rate product is used as reference. In order to assess the performance of PNPR v3 over the globe, an independent part of the observational database is used in a verification study. The good results found over all surface types (CC > 0.90, ME < −0.22 mm h−1, RMSE < 2.75 mm h−1 and FSE% < 100% for rainfall rates lower than 1 mm h−1 and around 30–50% for moderate to high rainfall rates), demonstrate the good outcome of the input selection procedure, as well as of the training and design phase of the neural network. For further verification, two case studies over Italy are also analysed and a good consistency of PNPR v3 retrievals with simultaneous ground radar observations and with the GMI GPROF V05 estimates is found. PNPR v3 is a global rainfall retrieval algorithm, able to optimally exploit the GMI multi-channel response to different surface types and precipitation structures, that provide global rainfall retrieval in a computationally very efficient way, making the product suitable for near-real time operational applications.

Observation of intrinsically bright terrestrial gamma ray flashes from the Mediterranean basin

We present three terrestrial gamma ray flashes (TGFs) observed over the Mediterranean basin by the Reuven Ramaty High Energy Solar Spectroscope Imager (RHESSI) satellite. Since the occurrence of these events in the Mediterranean region is quite rare, the characterization of the events was optimized by combining different approaches in order to better define the cloud of origin. The TGFs on 7 November 2004 and 16 October 2006 came from clouds with cloud top higher than 10-12 km where often a strong penetration into the stratosphere is found. This kind of cloud is usually associated with heavy precipitation and intense lightning activity. Nevertheless, the analysis of the cloud type based on satellite retrievals shows that the TGF on 27 May 2004 was produced by an unusual shallow convection. This result appears to be supported by the model simulation of the particle distribution and phase in the upper troposphere. The TGF on 7 November 2004 is among the brightest ever measured by RHESSI. The analysis of the energy spectrum of this event is consistent with a production altitude ≤12 km, which is in the upper part of the cloud, as found by the meteorological analysis of the TGF-producing thunderstorm. This event must be unusually bright at the source in order to produce such a strong signal in RHESSI. We estimate that this TGF must contain ∼3 × 10¹⁸ initial photons with energy >1 MeV. This is 1 order of magnitude brighter than earlier estimations of an average RHESSI TGF.