3 research outputs found
Enhancing Tropospheric Humidity Data Records from Satellite Microwave and Radiosonde Sensors
Water vapor is the most dominant greenhouse gas and plays a critical role in the climate by regulating the Earth's radiation budget and hydrological cycle. A comprehensive dataset is required to describe the temporal and spatial distribution of water vapor, evaluate the performance of climate and weather prediction models in terms of simulating tropospheric humidity, and understand the role of water vapor and its feedback in the climate system. Satellite microwave and radiosonde measurements are two important sources of tropospheric humidity. However, both datasets are subject to errors and uncertainties. The goal of this dissertation was to develop techniques for quantifying and correcting errors in both radiosonde and microwave satellite data. These techniques can be used to homogenize the datasets in order to develop tropospheric humidity climate data records.
The quality of operational radiosonde data were investigated for different sensor types. It was found that the use of a variety of sensors over the globe introduces temporal and spatial errors in the data. Further, it was shown that the daytime radiation dry bias, which is one the most important errors in radiosonde data, depends on both sensor type and radiosonde launch time. The error significantly decreases if daytime data are collected near sunrise or sunset.
Radiometric errors in satellite data were investigated using both intercomparison of coincident observations as well as validation versus high-quality radiosonde and global positioning system radio occultation data. The results showed that the data from recently launched microwave sounders have a good accuracy relative to each other and simulated data.
However, the absolute accuracy of the microwave satellite data can still not be validated due to the lack of reference measurements. In addition, a novel technique for correcting geolocation errors in microwave satellite data was developed based on the difference between ascending and descending observations along the coastlines. Using this method, several important errors including timing errors up to a few hundred milliseconds, and sensor mounting errors up to 1.2 degree were found in some of the microwave instruments.
Finally, since satellite data are indirect measurements, a method was developed to transform satellite radiances from different water vapor channels to layer averaged humidity. The technique is very fast because radiative transfer calculations are only required to determine the empirical coefficients
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Fast 3D Inhomogeneous Radiative Transfer Model Incorporating Aspherical Frozen Hydrometeors with Application to Precipitation Locking
A horizontally inhomogeneous unified microwave radiative transfer (HI-UMRT) model incorporating aspherical frozen hydrometeors based on the NASA/GSFC OpenSSP database is presented to study 3-dimensional (3D) effects of horizontal inhomogeneous clouds on computed microwave radiances and facilitate satellite radiance assimilation over horizontally inhomogeneous weather conditions. HI-UMRT provides a coupled two-Stokes parameter numerical radiance solution of the 3D radiative transfer equation by embedding the existing 1D UMRT algorithm into an iterative perturbation scheme. The horizontal derivatives in radiances of lower perturbation order are treated as the source functions of the azimuthal harmonic perturbation radiative transfer equations that are readily solved by the planar-stratified 1D UMRT algorithm.The 1D UMRT algorithm requires symmetry of the transition matrix for the discretized planar-stratified radiative transfer equation to realize numerically stable and accurate matrix operations as required by the discrete-ordinate eigenanalysis method. In this thesis, the necessary block-diagonal structure of the full Stokes matrix for randomly oriented OpenSSP aspherical hydrometeors is shown to be maintained, albeit with small asymmetric deviations which introduce small asymmetric components into the transition matrix that are negligible for most passive microwave remote sensing applications. An upper bound of the brightness temperature error calculated by neglecting the asymmetric components of the transition matrix under even extreme atmospheric conditions is shown to be small. Hence the OpenSSP hydrometeor database can be reliably used within the UMRT model.Block-diagonal Stokes matrix elements along with other single-scattering parameters of OpenSSP hydrometeors were subsequently used in radiative simulations of multi-stream dual-polarization radiances for a simulated hurricane event to demonstrate the inherent numerical stability and utility of the extended 1D UMRT algorithm. An intercomparison of computed upwelling radiances for a multiphase distribution of aspherical OpenSSP hydrometeors versus a mass-equivalent Mie hydrometeor polydispersion for key sensing frequencies from 10 to 874 GHz shows the considerable impact of complex (versus simple spherical) hydrometeors on predicted microwave radiances.Further, a numerical performance assessment shows that the increase in computing time for the 3D HI-UMRT model relative to the 1D UMRT model is moderate since (i) the computationally efficient UMRT engine is applied only to the perturbation equations with non-trivial solutions, and (ii) the layer parameters for the 1D solution are reused for all higher perturbation orders. Numerical simulations using HI-UMRT based on 3D cloud profiles simulated by the WRF numerical weather model illustrate the convergence of the iterative perturbation series. An intercomparison of top-of-atmosphere brightness temperature images for HI-UMRT versus the planar-stratified UMRT model illustrates the considerable impact of cloud horizontal inhomogeneities on computed upwelling microwave radiances.The microwave radiances simulated using UMRT at 118 and 183 GHz based on the Orbital Micro Systems Inc. Global Earth Monitoring System (GEMS) CubeSat constellation concept have been used in an all-weather microwave data assimilation scheme to facilitate precipitation locking of hydrometeor state variables in severe weather. The capability of first frame precipitation locking can be achieved based on constrained extended Kalman filtering (XKF), statistical estimation of a flow-dependent background error covariance matrix, and appropriate update of state variables using nonlinear iterative method. Preliminary simulation results demonstrate the potential for assimilating both thermodynamic and hydrometeor variables in first-frame locking iterations