Spaceborne Microwave Radiometry: Calibration, Intercalibration, and Science Applications.

Spaceborne microwave radiometry is the backbone for assimilation into numerical weather forecasts and provides important information for Earth and environment science. The extensive radiometric data must go through the process of calibration and intercalibration prior to science application. This work deals with the entire process by providing systematic methods and addressing critical challenges. These methods have been applied to NASA and JAXA’s Global Precipitation Measurement (GPM) mission and many other radiometers to make important contributions and to solve long-standing issues with coastal science applications. Specifically, it addresses four important challenges: 1) improving cold calibration with scan dependent characterization; 2) reducing the uncertainty of warm calibration; 3) deriving calibration dependence across the full range of brightness temperatures with both cold and warm calibration; and 4) investigating calibration variability and dependence on geophysical parameters. One critical challenge in science applications of radiometer data is that coastal science products from radiometers have previously been largely unavailable due to land contamination. We therefore develop methods to correct for land contamination and derive coastal science products. This thesis addresses these challenges by developing their solutions and then applying them to the GPM mission and its radiometer constellation.PhDAtmospheric, Oceanic and Space SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120728/1/johnxun_1.pd

Precipitation products from the hydrology SAF

Abstract. The EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) was established by the EUMETSAT Council on 3 July 2005, starting activity on 1 September 2005. The Italian Meteorological Service serves as Leading Entity on behalf of twelve European member countries. H-SAF products include precipitation, soil moisture and snow parameters. Some products are based only on satellite observations, while other products are based on the assimilation of satellite measurements/products into numerical models. In addition to product development and generation, H-SAF includes a product validation program and a hydrological validation program that are coordinated, respectively, by the Italian Department of Civil Protection and by the Polish Institute of Meteorology and Water Management. The National Center of Aeronautical Meteorology and Climatology (CNMCA) of the Italian Air Force is responsible for operational product generation and dissemination. In this paper we describe the H-SAF precipitation algorithms and products, which have been developed by the Italian Institute of Atmospheric Sciences and Climate (in collaboration with the international community) and by CNMCA during the Development Phase (DP, 2005–2010) and the first Continuous Development and Operations Phase (CDOP-1, 2010–2012). The precipitation products are based on passive microwave measurements obtained from radiometers onboard different sun-synchronous low-Earth-orbiting satellites (especially, the SSM/I and SSMIS radiometers onboard DMSP satellites and the AMSU-A + AMSU-B/MHS radiometer suites onboard EPS-MetOp and NOAA-POES satellites), as well as on combined infrared/passive microwave measurements in which the passive microwave precipitation estimates are used in conjunction with SEVIRI images from the geostationary MSG satellite. Moreover, the H-SAF product generation and dissemination chain and independent product validation activities are described. Also, the H-SAF program and its associated activities that currently are being carried out or are planned to be performed within the second CDOP phase (CDOP-2, 2012–2017) are presented in some detail. Insofar as CDOP-2 is concerned, it is emphasized that all algorithms and processing schemes will be improved and enhanced so as to extend them to satellites that will be operational within this decade – particularly the geostationary Meteosat Third Generation satellites and the low-Earth-orbiting Core Observatory of the international Global Precipitation Measurement mission. Finally, the role of H-SAF within the international science and operations community is explained.</p

The September 2019 floods in Spain: An example of the utility of satellite data for the analysis of extreme hydrometeorological events

Major floods in Spain in September 9–13, 2019 resulted in seven casualties and massive losses to agriculture, property and infrastructure. This paper investigates the utility of satellite data to: (1) characterize the event when input into a hydrological model, and to provide an accurate picture of the evolution of the floods; and (2) inform meteorologists in real time in order to complement model forecasts. It is shown that the precipitation estimates from the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO, available since 2014) and the merged satellite estimates provide an extraordinary improvement over previous technologies to monitor severe hydrometeorological episodes in near real time. In spite of known biases and errors, these new satellite precipitation estimates can be of broad practical interest to deal with emergencies and long-term readiness, especially for semi-arid areas potentially affected by ongoing global warming. Comparisons of satellite data of the September event with model outputs and more direct observations such as rain gauges and ground radars reinforce the idea that satellites are fundamental for an appropriate management of hydrometeorological events.Funding from projects PID2019-108470RB-C21, PID2019-108470RB-C22 (AEI/FEDER, UE), CGL2016-80609-R, and 1365002970/KMA2018-00721 (Korean Meteorological Agency, Korea) is gratefully acknowledged

Error in hydraulic head and gradient time-series measurements: a quantitative appraisal

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Abstract.&amp;lt;/strong&amp;gt; Hydraulic head and gradient measurements underpin practically all investigations in hydro(geo)logy. There is sufficient information in the literature to suggest that head measurement errors may be so large that flow directions can not be inferred reliably, and that their magnitude can have as great an effect on the uncertainty of flow rates as the hydraulic conductivity. Yet, educational text books contain limited content regarding measurement techniques and studies rarely report on measurement errors. The objective of our study is to review currently-accepted standard operating procedures in hydrological research and to determine the smallest head gradients that can be resolved. To this aim, we first systematically investigate the systematic and random measurements errors involved in collecting time series information on hydraulic head at a given location: (1) geospatial position, (2) point of head, (3) depth to water, and (4) water level time series. Then, by propagating the random errors, we find that with current standard practice, horizontal head gradients&amp;amp;#8201;&amp;lt;&amp;amp;#8201;10&amp;lt;sup&amp;gt;&amp;amp;#8722;4&amp;lt;/sup&amp;gt; are resolvable at distances&amp;amp;#8201;&amp;amp;#10886;&amp;amp;#8201;170&amp;amp;#8201;m. Further, it takes extraordinary effort to measure hydraulic head gradients&amp;amp;#8201;&amp;lt;&amp;amp;#8201;10&amp;lt;sup&amp;gt;&amp;amp;#8722;3&amp;lt;/sup&amp;gt; over distances&amp;amp;#8201;&amp;lt;&amp;amp;#8201;10&amp;amp;#8201;m. In reality, accuracy will be worse than our theoretical estimates because of the many possible systematic errors. Regional flow on a scale of kilometres or more can be inferred with current best-practice methods, but processes such as vertical flow within an aquifer cannot be determined until more accurate and precise measurement methods are developed. Finally, we offer a concise set of recommendations for water level, hydraulic head and gradient time series measurements. We anticipate that our work contributes to progressing the quality of head time series data in the hydro(geo)logical sciences, and provides a starting point for the development of universal measurement protocols for water level data collection.&amp;lt;/p&amp;gt; </jats:p

CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017

The construction of Brand Denmark:A case study of the reversed causality in nation brand valuation

In this article we unpack the organizational effects of the valuation practices enacted by nation branding rankings in a contemporary case where the Danish government employed branding-inspired methods. Our main argument is that the use of nation branding was enabled by the Nation Brands Index via its efficient translation of fuzzy political goals into understandable numerical objectives. The Nation Brands Index becomes the driving force in a powerful bureaucratic translation of nation branding which in turn has several reordering effects at organizational level. We thus demonstrate how the Nation Brands Index permits bureaucratic expansion in central government administration as it continuously maintains and reconstructs problems solvable by the initiation of more nation branding initiatives and projects and hence more bureaucratic activity

CIRA annual report FY 2015/2016

Reporting period April 1, 2015-March 31, 2016

BENCHMARKING BAYESIAN DEEP LEARNING METHODS WITH MULTI-SPECTRAL SATELLITE IMAGERY

The deep convolutional neural network (DCNN) is the current state-of-the-art approach for automatic image classification tasks. Historically, Bayesian deep learning methods have been applied to these models in narrow scopes. This thesis has created and tested several Bayesian deep learning models to perform classification on operational meteorological multi-spectral satellite data while quantifying the uncertainty in the model predictions. This large-scale dataset is used to compare the performance of Bayesian models against a DCNN and the current algorithm used by the National Aeronautics and Space Administration (NASA) to perform precipitation classification on the dataset. The use of a large-scale, operational dataset to benchmark Bayesian deep learning methods is the first application of its kind and represents a novel contribution to the fields of Bayesian deep learning and computer science. Several novel benchmarks were developed for use in this work. The best performing Bayesian model achieved 92 percent classification accuracy with demonstrated calibrated uncertainty on test data. All Bayesian models are shown to outperform current state-of-the-art DCNNs and the current operational algorithm. Furthermore, it is demonstrated that Bayesian model uncertainties can be used to screen uncertain predictions, and these uncertainties can be mapped spatially to identify specific regions of data that can be used to further improve the model performance.Captain, United States Marine CorpsApproved for public release; distribution is unlimited

Hydrologic modeling with remote sensing for the estimation of groundwater resources within the Sand River Catchment, South Africa

The Sand River Catchment is an important tributary of the transboundary Limpopo River in South Africa, which spans Botswana, Mozambique, South Africa, and Mozambique. Groundwater is a critical resource in the region, especially in the context of population growth and climate change. Data are needed for proper management of these water resources. In regions where groundwater data are sparse in time, space, or both, the most promising solutions come from satellites and hydrologic models. Regional literature suggests that the Soutpansberg Mountains, located within the Sand Catchment, are high-elevation water towers with uncertain groundwater resources. Improved understanding of groundwater resources in this watershed is critical for water resources management in downstream areas of the Sand River catchment. Groundwater resources in the Soutpansberg Mountains watershed were estimated via a hydrologic modelling and catchment water balance approach and validated with field data using electrical resistivity tomography. Groundwater data were obtained from NASA’s Gravity Recovery and Climate Experiment (GRACE). Precipitation and surface water data were obtained from the South Africa Department of Water and Sanitation (DWS) gage network. Additional data for surface water components were obtained from the Global Land Data Assimilation System (GLDAS) that combines satellite and ground-based data with land surface models and data assimilation. Flow and infiltration were modelled using HEC-HMS (U.S. Army Corps of Engineers). The model and water balance results support the hypothesis that the Soutpansberg Mountains watershed is a high recharge area that requires monitoring for sustainable use

Laboratory for Atmospheres 2007 Technical Highlights

The 2007 Technical Highlights describes the efforts of all members of the Laboratory for Atmospheres. Their dedication to advancing Earth Science through conducting research, developing and running models, designing instruments, managing projects, running field campaigns, and numerous other activities, is highlighted in this report