Browsing by Author "Vonder Haar, Thomas H., committee member"
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Item Open Access A spatio-temporal correlation technique to improve satellite rainfall accumulation(Colorado State University. Libraries, 2011) Petković, Veljko, author; Kummerow, Christian D., advisor; Vonder Haar, Thomas H., committee member; Ramírez, Jorge A., committee memberA spatio-temporal correlation technique has been developed to combine satellite rainfall measurements using the spatial and temporal correlation of the rainfall fields to overcome problems of sparse and infrequent measurements, while at the same time accounting for the measurements' accuracies. This technique estimates instantaneous rainfall with desired temporal sampling using only currently available satellite measurements with the goal of estimating 3-hour total rainfall accumulations at various spatial scales. The technique uses weighted mean to combine the measurements, adjusting the weights to the temporal correlation length of the measured rainfall field, and to the instrument accuracies. The relationship between the temporal and spatial correlation of the rainfall field is exploited to provide information about rainfall beyond instantaneous measurements. This information, depending on the nature of the rainfall field, can be accurate for prolonged time periods. It is shown that slow changing rainfall fields (i.e. stratiform-like rain) have high values of spatial correlation coefficients, and temporal correlation lengths as long as 60min. While, on the other hand, fast changing rainfall fields (i.e. convective-like rain) tend to have low spatial correlations, and temporal correlation lengths as short as 20min. This technique is developed using synthetic radar data. Nine months of the Operational Program for the Exchange of weather RAdar (OPERA) data is used on grid sizes of 100km, 250km and 500km with pixel resolutions of 8km, 12km and 24km to simulate satellite FOVs, and then applied to the real satellite data over the Southwest region of USA to calculate 3-hour rainfall accumulations. The results are then compared to the simple averaging technique , which takes a simple mean of the measurements as a constant rainfall rate over the entire accumulation period. The comparison is presented as improvements of the total absolute and RMS errors. Using synthetic data, depending on the time separation of the measurements and their accuracy, the technique has shown the potential to bring improvements of up to 40% in absolute, and up to 25% in RMS error. When applied to the real satellite data over the SE-USA, the technique has shown less skill, only 2% to 6% error improvement, which can be explained by the poor temporal sampling of the reference measurements. This technique is computationally inexpensive and easily applicable to currently used rainfall accumulation methods with linear interpolation between measurements such as CMORPH (Climate Prediction Center's Morphing Technique) and TMPA (The Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis).Item Open Access Building the foundations for a physically based passive microwave precipitation retrieval algorithm over the US Southern Great Plains(Colorado State University. Libraries, 2015) Ringerud, Sarah, author; Kummerow, Christian D., advisor; Peters-Lidard, Christa D., advisor; Reising, Steven C., committee member; van den Heever, Susan C., committee member; Vonder Haar, Thomas H., committee memberThe recently launched NASA Global Precipitation Measurement Mission (GPM) offers the opportunity for a greatly increased understanding of global rainfall and the hydrologic cycle. The GPM algorithm team has made improvements in passive microwave remote sensing of precipitation over land a priority for this mission, and implemented a framework allowing for algorithm advancement for individual land surface types as new techniques are developed. In contrast to the radiometrically cold ocean surface, land emissivity in the microwave is large with highly dynamic variability. An accurate understanding of the instantaneous, dynamic emissivity in terms of the associated surface properties is necessary for a physically based retrieval scheme over land, along with realistic profiles of frozen and liquid hydrometeors. In an effort to better simulate land surface microwave emissivity, a combined modeling technique is developed and tested over the US Southern Great Plains (SGP) area. The National Centers for Environmental Prediction (NCEP) Noah land surface model is utilized for surface information, with inputs optimized for SGP. A physical emissivity model, using land surface model data as input, is used to calculate emissivity at the 10 GHz frequency, combining contributions from the underlying soil and vegetation layers, including the dielectric and roughness effects of each medium. An empirical technique is then applied, based upon a robust set of observed channel covariances, extending the emissivity calculations to all channels. The resulting emissivities can then be implemented in calculation of upwelling microwave radiance, and combined with ancillary datasets to compute brightness temperatures (Tbs) at the top of the atmosphere (TOA). For calculation of the hydrometeor contribution, reflectivity profiles from the Tropical Rainfall Measurement Mission Precipitation Radar (TRMM-PR) are utilized along with coincident Tbs from the TRMM radiometer (TMI), and cloud resolving model data from NASA-Goddard's MMF model. Ice profiles are modified to be consistent with the higher frequency microwave Tbs. Resulting modeled TOA Tbs show correlations to observations of 0.9 along with biases 1K or less and small RMS error and show improved agreement over the use of climatological emissivity values. The synthesis of the emissivity and cloud resolving model input with satellite and ancillary datasets leads to creation of a unique Tb database for SGP that includes both dynamic surface and atmospheric information physically consistent with the LSM, emissivity model, and atmospheric information, for use in a Bayesian-type precipitation retrieval scheme utilizing a technique that can easily be applied to GPM as data becomes available.Item Open Access Improving the quality of extreme precipitation estimates using satellite passive microwave rainfall retrievals(Colorado State University. Libraries, 2017) Petković, Veljko, author; Kummerow, Christian D., advisor; Vonder Haar, Thomas H., committee member; Rutledge, Steven A., committee member; Niemann, Jeffrey D., committee memberSatellite rainfall estimates are invaluable in assessing global precipitation. As a part of the Global Precipitation Measurement (GPM) mission, a constellation of orbiting sensors, dominated by passive microwave imagers, provides a full coverage of the planet approximately every 2-3 hours. Several decades of development have resulted in passive microwave rainfall retrievals that are indispensable in addressing global precipitation climatology. However, this prominent achievement is often overshadowed by the retrieval's performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate rainfall measurements. This is especially true over land, where rainfall estimates are based on an observed mean relationship between high frequency (e.g., 89 GHz) brightness temperature (Tb) depression (i.e., the ice-scattering signature) and rainfall rate. In the first part of this study, an extreme precipitation event that caused historical flooding over south-east Europe is analyzed using the GPM constellation. Performance of the rainfall retrieval is evaluated against ground radar and gage reference. It is concluded that satellite observations fully address the temporal evolution of the event but greatly underestimate total rainfall accumulation (by factor of 2.5). A primary limitation of the rainfall algorithm is found to be its inability to recognize variability in precipitating system structure. This variability is closely related to the structure of the precipitation regime and the large-scale environment. To address this influence of rainfall physics on the overall retrieval bias, the second part of this study utilizes TRMM radar (PR) and radiometer (TMI) observations to first confirm that the Tb-to-rain-rate relationship is governed by the amount of ice in the atmospheric column. Then, using the Amazon and Central African regions as testbeds, it demonstrates that the amount of ice aloft is strongly linked to a precipitation regime. A correlation found between the large-scale environment and precipitation regimes is then further examined. Variables such as Convective Available Potential Energy (CAPE), Cloud Condensation Nuclei (CCN), wind shear, and vertical humidity profiles are found to be capable of predicting a precipitation regime and explaining up to 40% of climatological biases. Dry over moist air conditions are favorable for developing intense, well organized systems such as MCSs in West Africa and the Sahel. These systems are characterized by strong Tb depressions and above average amounts of ice aloft. As a consequence, microwave retrieval algorithms misinterpret these non-typical systems assigning them unrealistically high rainfall rates. The opposite is true in the Amazon region, where observed raining systems exhibit relatively little ice while producing high rainfall rates. Based on these findings, in the last part of the study, the GPM operational retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. When forming an estimate, the modified algorithm is allowed to use this ancillary information to filter out a priori states that do not match the general environmental condition relevant to the observation and thus reduce the difference between the assumed and observed variability in ice-to-rain ratio. The results are compared to the ground Multi-Radar Multi-Sensor (MRMS) network over the US at various spatial and temporal scales demonstrating outstanding potentials in improving the accuracy of rainfall estimates from satellite-borne passive microwave sensors over land.Item Open Access Integration, characterization, and calibration of the high-frequency airborne microwave and millimeter-wave radiometer (HAMMR) instrument(Colorado State University. Libraries, 2014) Johnson, Thaddeus, author; Reising, Steven C., advisor; Morton, Yu, committee member; Vonder Haar, Thomas H., committee member; Kangaslahti, Pekka, committee memberCurrent satellite ocean altimeters include nadir-viewing, co-located 18-34 GHz microwave radiometers to measure wet-tropospheric path delay. Due to the large antenna footprint sizes at these frequencies, the accuracy of wet path retrievals is substantially degraded within 40 km of coastlines, and retrievals are not provided over land. A viable approach to improve their capability is to add wide-band millimeter-wave window channels in the 90-183 GHz band, thereby achieving finer spatial resolution for a fixed antenna size. In this context, the upcoming Surface Water and Ocean Topography (SWOT) mission is in formulation and planned for launch in late 2020 to improve satellite altimetry to meet the science needs of both oceanography and hydrology and to transition satellite altimetry from the open ocean into the coastal zone and over inland water. To address wet-path delay in these regions, the addition of 90-183 GHz millimeter-wave window-channel radiometers to current Jason-class 18-34 GHz radiometers, is expected to improve retrievals of wet-tropospheric delay in coastal areas and to enhance the potential for over-land retrievals. To this end, an internally-calibrated, wide-band, cross-track scanning airborne microwave and millimeter-wave radiometer is being developed in collaboration between Colorado State University (CSU) and Caltech/NASA's Jet Propulsion Laboratory (JPL). This airborne radiometer includes microwave channels at 18.7, 23.8, and 34.0 GHz at both H and V polarizations; millimeter-wave window channels at 90, 130, 168 GHz; and temperature and water vapor sounding channels adjacent to the 118 and 183 GHz absorption lines, respectively. Since this instrument is demonstrating this technology for the potential use in future Earth science missions, substantial effort has been put into ensuring the instrument has a minimal mass and volume and is robust and well characterized. To this end the optical alignment has been extensively tested and characterized and a novel blackbody calibration target has been designed and integrated into the system. All supporting sub-systems such as power distribution and data acquisition have been integrated into the chassis allowing the instrument to be easily run by a single operator. Preliminary test flights have been done that demonstrate the reliability and robustness of this instrument as well as demonstrating the increased special resolution of the millimeter-wave window and sounding channels over that of the Jason-class 18-34 GHz radiometers.Item Open Access Quantitative comparisons of satellite observations and cloud models(Colorado State University. Libraries, 2011) Wang, Fang, author; Kummerow, Christian D., advisor; Vonder Haar, Thomas H., committee member; Cotton, William R., committee member; Ramirez, Jorge A., committee memberMicrowave radiation interacts directly with precipitating particles and can therefore be used to compare microphysical properties found in models with those found in nature. Lower frequencies (< 37 GHz) can detect the emission signals from the raining clouds over radiometrically cold ocean surfaces while higher frequencies (≥ 37 GHz) are more sensitive to the scattering of the precipitating-sized ice particles in the convective storms over high-emissivity land, which lend them particular capabilities for different applications. Both are explored with a different scenario for each case: a comparison of two rainfall retrievals over ocean and a comparison of a cloud model simulation to satellite observations over land. Both the Goddard Profiling algorithm (GPROF) and European Centre for Medium-Range Weather Forecasts (ECMWF) one-dimensional + four-dimensional variational analysis (1D+4D-Var) rainfall retrievals are inversion algorithms based on the Bayes' theorem. Differences stem primarily from the a-priori information. GPROF uses an observationally generated a-priori database while ECMWF 1D-Var uses the model forecast First Guess (FG) fields. The relative similarity in the two approaches means that comparisons can shed light on the differences that are produced by the a-priori information. Case studies have found that differences can be classified into four categories based upon the agreement in the brightness temperatures (Tbs) and in the microphysical properties of Cloud Water Path (CWP) and Rain Water Path (RWP) space. We found a category of special interest in which both retrievals converge to similar Tb through minimization procedures but produce different CWP and RWP. The similarity in Tb can be attributed to comparable Total Water Path (TWP) between the two retrievals while the disagreement in the microphysics is caused by their different degrees of constraint of the cloud/rain ratio by the observations. This situation occurs frequently and takes up 46.9% in the one month 1D-Var retrievals examined. To attain better constrained cloud/rain ratios and improved retrieval quality, this study suggests the implementation of higher microwave frequency channels in the 1D-Var algorithm. Cloud Resolving Models (CRMs) offer an important pathway to interpret satellite observations of microphysical properties of storms. High frequency microwave brightness temperatures (Tbs) respond to precipitating-sized ice particles and can, therefore, be compared with simulated Tbs at the same frequencies. By clustering the Tb vectors at these frequencies, the scene can be classified into distinct microphysical regimes, in other words, cloud types. The properties for each cloud type in the simulated scene are compared to those in the observation scene to identify the discrepancies in microphysics within that cloud type. A convective storm over the Amazon observed by the Tropical Rainfall Measuring Mission (TRMM) is simulated using the Regional Atmospheric Modeling System (RAMS) in a semi-ideal setting, and four regimes are defined within the scene using cluster analysis: the 'clear sky/thin cirrus' cluster, the 'cloudy' cluster, the 'stratiform anvil' cluster and the 'convective' cluster. The relationship between Tb difference of 37 and 85 GHz and Tb at 85 GHz is found to contain important information of microphysical properties such as hydrometeor species and size distributions. Cluster-by-cluster comparison between the observations and the simulations discloses biases in the model including overproduction of supercooled water and large hail particles. The detected biases shed light on how the model should be adjusted to generate more realistic microphysical relationships for each cluster. Guided by the model/observation discrepancies in the 'convective' cloud cluster, a new simulation is performed to provide dynamic adjustments by generating more but smaller hail particles.