William Linwood Jones

This paper describes the benefits of using a microwave radiative transfer model (RTM) to improve the inter-satellite radiometric calibration (XCAL) between two independent satellite microwave radiometers. Because this work was sponsored by the NASA Global Precipitation Mission, the emphasis of this paper is on radiometer channels that are used for atmospheric precipitation retrievals; however, this technique is applicable for microwave remote sensing in general, over a wide range of satellite remote-sensing applications. An XCAL example is presented for the NASA Global Precipitation Mission, whereby the GPM Microwave Imager is used to calibrate another microwave radiometer (TROPICS) within the GPM constellation of satellites. This approach involves intercomparing near-simultaneous measured brightness temperatures from these radiometers viewing a common homogeneous ocean scene. The double difference between observed and theoretical brightness temperature, derived using a radiative transfer model, is used to establish a radiometric calibration offset or bias. On-orbit comparisons are presented for two different approaches, namely, with and without the aid of the RTM. The results demonstrate significant improvements in the XCAL biases derived when using the RTM, and this is especially beneficial when one radiometer produces anomalous brightness temperatures.

Typically, every 3 - 5 years, the brightness temperature data products for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) were reprocessed to incorporate improvements in signal processing algorithms. In 2017, the TMI 1B data product, Global Precipitation Measurement (GPM) version 5 (V05), was updated with an improved correction for a slightly emissive main reflector antenna that was physics-based and it incorporated empirical coefficients derived from additional deep-space calibration tests performed. Subsequently, it was discovered that TMI brightness temperatures still contain a consistent bias of the order of 0.6 K in all channels that varied with orbital phase and sun angle in a way that resembled the orbital pattern of the estimated main reflector physical temperature. In this paper, we present an analysis of this bias and provide a correction, which has been included in GPM version 7 (V07) reprocessing. First, an updated inter-satellite radiometric calibration (XCAL) radiative transfer model was used to derive the physical temperature of TMI’s main reflector for all possible solar orientations throughout the 17-year mission. Then, the corrected TMI brightness temperatures were derived using these new physical temperature tables. Finally, these updated Tbs were validated by intercomparison with collocated 13-month GMI measurements using the XCAL double difference technique. A quantitative and qualitative analysis of the improvements is provided.

The objective of the Hurricane Imaging Radiometer (HIRAD) is to produce wide-swath images of hurricane wind and rain fields during a single pass from a high-altitude aircraft. This instrument could be a prototype for the next generation of airborne hurricane remote sensors that operate on NOAA/USAF surveillance flights over named storms and hurricanes. The improved two-dimensional surface wind field measurements provided by the HIRAD approach are crucial to improved forecasts and warnings. For almost a decade, HIRAD has been used in research flights over hurricanes; however, because of various hardware issues, the scientific potential of its measurements has not been fulfilled. This paper presents a reanalysis of HIRAD measurements over Hurricane Gonzalo on 17 October 2014 that demonstrate remarkable results. The basis for this novel approach is to use coincident surface wind speed (WS) and rain rate (RR) measurements from another source to calibrate the HIRAD brightness temperature measurements. As a result, the HIRAD retrievals of WS and RR are in excellent agreement with the accompanying airborne remote sensors and in situ surface wind speed measurements, which validates the HIRAD technique proof of concept.

This paper presents a combined active/passive ocean vector wind (OVW) retrieval from the Global Precipitation Measurement Core Observatory (GPM-CO). Although the GPM-CO was designed to observe global precipitation, we demonstrate the potential for providing a climate quality OVW data product for the multi-decade GPM- TRMM era. Development of GMFs for the Ku- and Ka-band dual frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI) remote sensors onboard the GPM-CO is presented. The unique GPM active/passive OVW retrieval algorithm is described, which uses the modified 2nd Stokes parameter (linear combination of V & H-pol Tb's) to cancel the atmosphere and to enhances the anisotropic WD signal superimposed on GMI Tbs. The PR and GMI GMF's were then combined in a maximum likelihood estimation (MLE) algorithm to infer the OVW. Finally, this OVW retrieval algorithm was validated by comparing retrievals with collocated NASA Advanced Scatterometer (ASCAT) wind vectors. The statistics of WS and WD retrieval accuracy performance are presented and are shown to be comparable with those of conventional scatterometer and polarimetric radiometer missions.

This paper deals with the validation of rain rate and wind speed measurements from the High-Altitude Wind and Rain Airborne Profiler (HIWRAP), which occurred in September 2013 when the NASA Global Hawk unmanned aerial vehicle passed over an ocean rain squall line in the Gulf of Mexico near the North Florida coast. The three-dimensional atmospheric rain distribution and the associated ocean surface wind vector field were simultaneously measured by two independent remote sensing and two in situ systems, namely the ground-based National Weather Service Next-Generation Weather Radar (NEXRAD); the European Space Agency satellite Advanced Scatterometer (ASCAT), and two instrumented weather buoys. These independent measurements provided the necessary data to calibrate the HIWRAP radar using the measured ocean radar backscatter and to validate the HIWRAP rain and wind vector retrievals against NEXRAD, ASCAT and ocean buoys observations. In addition, this paper presents data processing procedures for the HIWRAP instrument, including the development of a geometric model to collocate time-morphed rain rates from the NEXRAD radar with HIWRAP atmospheric rain profiles. Results of the rain rate intercomparison are presented, and they demonstrate excellent agreement with the NEXRAD time-interpolated rain volume scans. In our analysis, we find that HIWRAP produces wind and rain rates that are consistent with the supporting ground and satellite estimates, thereby providing validation of the geolocation, the calibration, and the geophysical retrieval algorithms for the HIWRAP instrument.

This paper presents the results of the first characterization of coincident Ku- and Ka-band ocean surface normalized radar cross section measurements at earth incidence angles 0°–18° using one year of wide swath Global Precipitation Measurement (GPM) mission dual frequency precipitation radar (DPR) data. Empirical geophysical model functions were derived for both bands, isotropic and directorial sensitivity were assessed, and finally, sea surface temperature (SST) dependence of radar backscatter, at both bands, were investigated. The Ka-band exhibited higher vector wind sensitivity for a low-to-moderate wind speeds regime, and the SST effects were also observed to be substantially larger at Ka-band than at Ku-band.

This paper presents a novel ocean vector wind (OVW) retrieval algorithm for remote sensing of ocean winds, using the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) that operated for > 17 years on the Tropical Rainfall Measurement Mission (TRMM) observatory. In this study, the wind speed and anisotropic wind direction characteristics of the ocean backscatter and brightness temperature (Tb) were empirically characterized to produce geophysical model functions (GMFs) for 49 PR beam positions and three TMI frequency channels (10, 19 & 37 GHz). For passive TMI measurements, the modified 2nd Stokes parameter (weighted difference of V & H-pol Tbs) was used to cancel the atmosphere contribution, which enhances the wind direction signal. Thus, combining linearly polarized Tb measurements with the anisotropic backscatter signature of the Ku-band PR, the feasibility of retrieving ocean surface wind vector is demonstrated. The OVW retrieval algorithm is validated by comparing retrievals with collocated numerical weather models (GDAS) wind vectors, and the statistics of OVW retrieval accuracy is presented.

The National Aeronautics and Space Administration’s (NASA) Precipitation Measurement Missions (PMMs) include two earth satellite missions, namely, the Tropical Rainfall Measuring Mission (TRMM, 1997–2015) and the Global Precipitation Measurement (GPM, 2014-present). To generate a consistent multi-decadal brightness temperature (Tb) record that spans the TRMM and GPM eras, it is highly desirable to perform a comprehensive intercalibration of the TRMM Microwave Imager (TMI) and the GPM Microwave Imager (GMI) Tb measurements. Unfortunately, GMI and TMI share a limited common operational period of only 13 months. Fortunately, the WindSat polarimetric radiometer (2003-present) has been shown to be well calibrated and radiometrically stable relative to TMI for a period of over 5 years. Therefore, this paper describes the use of overlapping WindSat Tb measurements as the calibration bridge to achieve a seamless transfer joining the TMI and GMI Tb time series. Also, the development of the Tb measurement uncertainty estimation model is presented, which incorporates all relevant sources of uncertainty. Afterwards, this model was applied to three intercalibration processes: TMI to GMI, TMI to WindSat, and WindSat to GMI, and results are presented that quantify the corresponding Tb channel measurements biases and associated uncertainties associated with the merged TMI-GMI Tb record. This is an important accomplishment because this study can enable improved future Earth Science and global climate change investigations by making a long-term Tb record with estimated uncertainty available.

The airborne Hurricane Imaging Radiometer (HIRAD) was developed to remotely sense hurricane surface wind speed (WS) and rain rate (RR) from a high-altitude aircraft. The approach was to obtain simultaneous brightness temperature measurements over a wide frequency range to independently retrieve the WS and RR. In the absence of rain, the WS retrieval has been robust; however, for moderate to high rain rates, the joint WS/RR retrieval has not been successful. The objective of this paper was to resolve this issue by developing an improved forward radiative transfer model (RTM) for the HIRAD cross-track viewing geometry, with separated upwelling and specularly reflected downwelling atmospheric paths. Furthermore, this paper presents empirical results from an unplanned opportunity that occurred when HIRAD measured brightness temperatures over an intense tropical squall line, which was simultaneously observed by a ground based NEXRAD (Next Generation Weather Radar) radar. The independently derived NEXRAD RR created the simultaneous 3D rain field “surface truth”, which was used as an input to the RTM to generate HIRAD modeled brightness temperatures. This paper presents favorable results of comparisons of theoretical and the simultaneous, collocated HIRAD brightness temperature measurements that validate the accuracy of this new HIRAD RTM.

In this paper, we describe the development of Geophysical Model Functions (GMF) for remote sensing of ocean vector winds using the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) that operated for > 17 years on the Tropical Rainfall Measurement Mission (TRMM) observatory. Wind speed and anisotropic wind direction characteristics of the ocean backscatter and brightness temperature were empirically characterized for 49 PR beam positions and three TMI frequency channels. Temporal stability and sea surface temperature (SST) dependency of the GMFs are also evaluated.