Description: In this paper, the oceanic radiometric calibration biases of the Advanced Microwave Scanning Radiometer-2 (AMSR2) onboard the Global Change Observation Mission-Water (GCOM-W1) are analyzed. The double difference ( DD ) approach is utilized to perform inter-sensor inter-calibration for AMSR2 with the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) as the reference radiometer. This technique utilizes radiative transfer model (RTM) simulations and near-simultaneous clear-sky ocean-only observed brightness temperatures from the two microwave radiometers to estimate and correct the radiometric biases of ocean scenes for AMSR2 on a channel by channel basis.

Description: With rising demand for smaller, lower mass microwave instruments, internal calibration using noise diodes is becoming increasingly more attractive for space-borne radiometer applications. Since noise diodes can exhibit on-orbit excess temperature drift, internally calibrated systems typically require vicarious on-orbit recharacterization. The GMI is the first instrument of its kind to include both internal (noise diodes) and external (hot load/cold sky) calibration systems. The dual-calibration system provides the unprecedented capability to directly measure transient behaviors in the hot load, cold sky view, and receiver nonlinearity. Furthermore, the behavior of the noise diodes can be directly evaluated, which may shed light on improvements to internal calibration for future missions. This paper directly examines the behavior of the GMI noise diodes using the hot load and cold sky views for the first 6 months of operations. Two of the seven channels with noise diodes have exhibited on-orbit noise diode excess temperature drift of about 1 K. The other noise diodes have remained exceptionally stable. The noise diodes are used to evaluate transient behaviors in the GMI hot load, cold sky view, and nonlinearity. The hot-load brightness temperature variation due to gradients is re-evaluated and shown to be smaller at the lower frequencies than at preflight calibration. Radio frequency interference (RFI) in the cold view is evaluated using the noise diode backup calibration. The on-orbit nonlinearity is trended over the first 6 months and shown to be stable over that time period.

Description: In this paper, we develop efficient deconvolution and super-resolution methodologies, and apply these techniques to reduce image blurring and distortion inherent in an aperture synthesis system. Such a system produces ringing at sharp edges and other transitions in the observed field. The conventional approach to suppressing sidelobes is to apply linear apodization, which has the undesirable side effect of degrading spatial resolution. We have developed an efficient total variation minimization technique based on Split Bregman deconvolution that reduces image ringing while sharpening the image and preserving information content. The model was generalized to include upsampling of deconvolved images to a higher resolution grid. Furthermore, a proposed multiframe super-resolution method is presented that is robust to image noise and noise in the point spread function, and leads to additional improvements in spatial resolution. Our super-resolution methodologies are based on current research in sparse optimization and compressed sensing, which lead to unprecedented efficiencies for solving image reconstruction problems.

Description: Ground-based microwave radiometers operating at frequencies near the 22.235 GHz (K-band) water vapor absorption line have been used extensively for remote sensing of water vapor in the troposphere, both the integrated amount and its profile. This paper explores the potential to use ground-based, zenith-pointing K-band radiometer measurements along with optimized background data sets consisting of radiosonde profiles to detect dynamic changes and gradients in water vapor profiles. To explore this capability, the HUMidity EXperiment 2011 (HUMEX11) was conducted at the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Site near Lamont, OK, USA. This enables the choice of appropriate retrieval parameters to monitor temporal changes in atmospheric water vapor profiles. The results of this study illustrate that in a retrieval algorithm both the choice of the size of the background data set measured near the radiometer measurement time and the choice of atmospheric layer thickness affect the ability to remotely sense dynamic changes in water vapor. In general, it is found that background data sets of larger size provide better accuracy in a statistical sense but inhibit the ability to detect gradients.

Description: Quantitative structural analysis is a useful approach for studying geologic structures. It is particularly important in remote and complex fold-thrust belts where outcrop data and high-quality seismic reflection images are challenging to obtain. In this study, we integrated terrestrial light detection and ranging (LiDAR), unmanned aerial vehicle (UAV), and Landsat 8 (L8) data to extract high-resolution topographic and surface geologic information and constrain interpretations of three-dimensional (3-D) seismic reflection data in the Hero Range of the Qaidam Basin (QB) in northwestern China. UAV images were used to obtain a digital elevation model (DEM) and to measure the orientation of sedimentary bedding. Terrestrial LiDAR data were used to generate high-resolution digital outcrops and to evaluate the accuracy of the UAV-based DEM. L8 images were used to distinguish different stratigraphic units. The random sample consensus (RANSAC) algorithm was adopted to ascertain the best-fit plane of bedding. The results show that UAV images can be used to construct a DEM with ${〈} 1 ,{text m}$ resolution and orthophotos with 0.15-m resolution. Collectively, these data improve the ability to identify and measure small exposures of bedding surfaces. The RANSAC algorithm improves the accuracy of measuring bedding orientations by removing erroneous selection points and facilitating the recognition of second-order variations in bedding orientation. The integrated analysis of remotely sensed and 3-D seismic data indicates that, of the three anticlines within the Hero Range, two are fault-propagation folds (the Shizigou and Youshashan anticlines) and one is associated with a pop-up structure (Ganchaigou anticline).

Description: Precipitation data at high spatio-temporal resolution are essential for hydrological, meteorological, and ecological research in local basins and regions. The coarse spatial resolution (0.25°) of Tropical Rainfall Measuring Mission (TRMM) 3B43 product is insufficient for practical requirements. In this paper, a multivariable geographically weighted regression (GWR) downscaling method was developed to obtain 1 km precipitation. The GWR method was compared with two other downscaling methods [univariate regression (UR) and multivariate regression (MR)] in terms of the performance of downscaled annual precipitation. Variables selection procedures were proposed for selecting appropriate auxiliary factors in all three downscaling methods. To obtain the monthly 1 km precipitation, two monthly downscaling strategies (annual-based fraction disaggregation method and monthly based GWR method) were evaluated. All analysis was tested in Gansu province, China with a semiarid to arid climate for three typical years. Validation with measurements from 24 rain gauge stations showed that the proposed GWR method performed consistently better than the UR and MR methods. Two monthly downscaling methods were efficient in deriving the monthly precipitation at 1 km. The former method faces the challenge of precipitation spatial heterogeneity and the derived monthly precipitation heavily depends on the annual downscaled results, which could lead to the accumulation of errors. The monthly based GWR method is suitable for downscaling monthly precipitation, but the accuracy of original TRMM 3B43 data would have large influence on downscaling results. It was demonstrated that the proposed method was effective for obtaining both annual and monthly TRMM 1 km precipitation with high accuracy.

Description: A new approach to retrieve sea surface wind speed (SWS) in tropical cyclones (TCs) from the Advanced Microwave Scanning Radiometer 2 (AMSR2) data is presented. Analysis of all six AMSR2 C- and X-band channel measurements over TCs is shown to efficiently help to separate the rain contribution. Corrected measurements at 6.9 and 10.65 GHz are then used to retrieve the SWS. Spatial and temporal collocation of AMSR2 and tropical rain measurement mission (TRMM) microwave instrument (TMI) data is then further used to empirically relate TMI rain rate (RR) product to RR estimates from AMSR2 in hurricanes. SWS estimates are validated with measurements from the stepped frequency microwave radiometer (SFMR). As further tested, more than 100 North Atlantic and North Pacific TCs are analyzed for the 2012–2014 period. Despite few particular cases, most SWS fields are in a very good agreement with TC center data on maximum wind speeds, radii of storm, and hurricane winds. As also compared, very high consistency between AMSR2 and L-band SMOS wind speed estimates are obtained, especially for the super typhoon Haiyan, to prove the high potential of AMSR2 measurements in TCs.

Description: CLOUDET, a cloud detection and estimation algorithm for passive microwave imagers and sounders is presented. CLOUDET is based on a naïve Bayes classifier and multilayer perceptron. It is applied to the special sensor microwave imager/sounder (SSMIS), and the ECMWF integrated forecast system (IFS) cloud liquid water information has been used to train the algorithm. CLOUDET is applicable to both ocean and land-surface types. CLOUDET has been developed and evaluated by employing two different groups of radiometric information, namely, the humidity channels near 183 GHz (humidity algorithm) and the window channels between 19 and 91 GHz (window algorithm). It has been revealed that both humidity and window algorithms can detect cloudy scenes over ocean at a confidence level of more than 90% (80% over land). The analysis indicates that the humidity algorithm has a better ability in detecting cloudy scenes over ocean than the window algorithm ( ${bf CSI=0.98}$ vs. ${bf CSI=0.93}$ ). The opposite is true over land-surface type, revealing a CSI of 0.85 by humidity algorithm as opposed to CSI of 0.88 by window algorithm. The estimation of cloud by the CLOUDET algorithm has also been very promising during the validation effort. In particular, the correlation coefficient obtained over ocean through the use of the window algorithm is 0.70 (MAE 0.04 mm and RMSE 0.09 mm). The presented algorithm CLOUDET can be served as a stand-alone tool to reject and identify the cloudy scenes as well as to estimate the cloud liquid water path amount prior to assimilating the radiances into numerical weather prediction model.

Description: Observations during the Dynamics of the Madden–Julian Oscillation (DYNAMO) experiment focused on sensing atmospheric parameters, including vertical moisture profiles, cloud structure, precipitation processes, and planetary boundary layer properties, all of which are important for understanding and modeling the Madden–Julian Oscillation (MJO). These observations were performed using a variety of in-situ and remote sensors, including the S-band polarimetric and Ka-band (S-PolKa) radar, deployed by the National Center for Atmospheric Research (NCAR), and a colocated University of Miami microwave radiometer (UM-radiometer) operating at 23.8 and 30.0 GHz. These instruments sampled approximately the same volumes of the atmosphere at a variety of azimuth and elevation angles. The principal goal of this study is to develop a new retrieval strategy to estimate slant water vapor path (SWP) and slant liquid water (SLW) using UM-radiometer measurements from zenith to low elevation angles at a variety of azimuth angles. Retrievals of SWP along the radar signal path help to determine the error in radar reflectivity due to water vapor absorption. The retrieval algorithm has been developed using the vapor–liquid water ratio (VLWR) as well as both modeled and measured brightness temperatures for zenith to low elevation angles. Observation system simulation experiment (OSSE) results and measured radiosonde data have been used to determine that the retrieval uncertainty is less than 5% for integrated water vapor (IWV) and less than 12% for integrated liquid water (ILW). OSSE results for SWP show that the retrieval uncertainty is less than 8% at 5° elevation angle and less than 5% at 7° and 9°, while the mean difference between SWP retrieved from radiometer measurements and those retrieved from the S-PolKa radar during the DYNAMO campaign is less than 10% at 5° elevation angle and less than 7.5% at 7° and - °. OSSE results for SLW show that the mean error is less than 24% for 5° elevation angle and less than 18% for 7° and 9°. Such retrievals of SWP and SLW help to characterize the distribution of water vapor and liquid water in the lower troposphere, which in turn may contribute to improvements in forecasting of convective initiation and precipitation.

Description: Soil moisture measurements are required to improve our understanding of hydrological processes and linkages between the Earth’s water, energy, and carbon cycles. The efficient retrieval of soil moisture depends on various factors among which soil dielectric mixing models are considered to be an important factor. The main objective of this work focuses on testing different dielectric models—Mironov et al. , Dobson et al. , Wang and Schmugge, and Hallikainen et al. —for soil moisture retrieval using the combined radar/radiometer (ComRAD) ground-based L-band simulator system, which serves as a simulator for the instruments on NASA’s soil moisture active passive (SMAP) mission scheduled for launch in early next year. The single-channel algorithm at H polarization ( SCA-H ) version of the tau-omega model was used for soil moisture retrieval. A summer field experiment was conducted in 2012 at the United States Department of Agriculture (USDA) test site from which ComRAD measurements and validation samples of soil moisture were collected using theta probes and in situ sensors. The highest performance statistics combination in terms of high correlation ( ${r}$ ), low root-mean-square error ( RMSE ), and least bias has been obtained with SCA-H using the Mironov dielectric model ( $r = 0.79$ ; $RMSE = 0.04;{m^3}/{m^3}$ ; $bias = 0.01$ ) followed by Dobson ( $r = 0.76$ ; $RMSE = 0.04;{m^3}/{m^3}$ , $bias = - 0.01$ ), Wang and Schmugge ( $r = 0.79$ ; $RMSE = 0.04;{m^3}/{m^3}$ , $bias = 0.02$ ) and Hallikainen ( $r = 0.76$ ; $RMSE = 0.06;{m^3}/{m^3}$ , $bias = 0.04$ ). Although the performance of the four dielectric models is relatively comparable, this analysis indicates that the Mironov dielectric model is marginally better than others for passive-only microwave soil moisture retrieval and could be a useful choice for SMAP satellite soil moisture retrieval.