RADAR-BASED STOCHASTIC PRECIPITATION NOWCASTING USING THE SHORT-TERM ENSEMBLE PREDICTION SYSTEM (STEPS) (CASE STUDY: PANGKALAN BUN WEATHER RADAR)

Nowcasting, or the short-term forecasting of precipitation, is urgently needed to support the mitigation circle in hydrometeorological disasters. Pangkalan Bun weather radar is single-polarization radar with a 200 km maximum range and which runs 10 elevation angles in 10 minutes with a 250 meters spatial resolution. There is no terrain blocking around the covered area. The Short-Term Ensemble Prediction System (STEPS) is one of many algorithms that is used to generate precipitation nowcasting, and is already in operational use. STEPS has the advantage of producing ensemble nowcasts, by which nowcast uncertainties can be statistically quantified. This research aims to apply STEPS to generate stochastic nowcasting in Pangkalan Bun weather radar and to analyze its advantages and weaknesses. Accuracy is measured by counting the possibility of detection and false alarms under the 5 dBZ threshold and plotting them in a relative operating characteristic (ROC) curve. The observed frequency and forecast probability is represented by a reliability diagram to evaluate nowcast reliability and sharpness. Qualitative analysis of the results showed that the STEPS ensemble produces smoothed reflectivity fields that cannot capture extreme values in an observed quasi-linear convective system (QLCS), but that the algorithm achieves good accuracy under the threshold used, up to 40 minutes lead time. The ROC shows a curved upper left-hand corner, and the reliability diagram is an almost perfect nowcast diagonal line

Radar and satellite observations of precipitation: space time variability, cross-validation, and fusion

2017 Fall.Includes bibliographical references.Rainfall estimation based on satellite measurements has proven to be very useful for various applications. A number of precipitation products at multiple time and space scales have been developed based on satellite observations. For example, the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center has developed a morphing technique (i.e., CMORPH) to produce global precipitation products by combining existing space-based observations and retrievals. The CMORPH products are derived using infrared (IR) brightness temperature information observed by geostationary satellites and passive microwave-(PMW) based precipitation retrievals from low earth orbit satellites. Although space-based precipitation products provide an excellent tool for regional, local, and global hydrologic and climate studies as well as improved situational awareness for operational forecasts, their accuracy is limited due to restrictions of spatial and temporal sampling and the applied parametric retrieval algorithms, particularly for light precipitation or extreme events such as heavy rain. In contrast, ground-based radar is an excellent tool for quantitative precipitation estimation (QPE) at finer space-time scales compared to satellites. This is especially true after the implementation of dual-polarization upgrades and further enhancement by urban scale X-band radar networks. As a result, ground radars are often critical for local scale rainfall estimation and for enabling forecasters to issue severe weather watches and warnings. Ground-based radars are also used for validation of various space measurements and products. In this study, a new S-band dual-polarization radar rainfall algorithm (DROPS2.0) is developed that can be applied to the National Weather Service (NWS) operational Weather Surveillance Radar-1988 Doppler (WSR-88DP) network. In addition, a real-time high-resolution QPE system is developed for the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) Dallas-Fort Worth (DFW) dense radar network, which is deployed for urban hydrometeorological applications via high-resolution observations of the lower atmosphere. The CASA/DFW QPE system is based on the combination of a standard WSR-88DP (i.e., KFWS radar) and a high-resolution dual-polarization X-band radar network. The specific radar rainfall methodologies at Sand X-band frequencies, as well as the fusion methodology merging radar observations at different temporal resolutions are investigated. Comparisons between rainfall products from the DFW radar network and rainfall measurements from rain gauges are conducted for a large number of precipitation events over several years of operation, demonstrating the excellent performance of this urban QPE system. The real-time DFW QPE products are extensively used for flood warning operations and hydrological modelling. The high-resolution DFW QPE products also serve as a reliable dataset for validation of Global Precipitation Measurement (GPM) satellite precipitation products. This study also introduces a machine learning-based data fusion system termed deep multi-layer perceptron (DMLP) to improve satellite-based precipitation estimation through incorporating ground radar-derived rainfall products. In particular, the CMORPH technique is applied first to derive combined PMW-based rainfall retrievals and IR data from multiple satellites. The combined PMW and IR data then serve as input to the proposed DMLP model. The high-quality rainfall products from ground radars are used as targets to train the DMLP model. In this dissertation, the prototype architecture of the DMLP model is detailed. The urban scale application over the DFW metroplex is presented. The DMLP-based rainfall products are evaluated using currently operational CMORPH products and surface rainfall measurements from gauge networks

Assessing the impact of non-conventional observation types on high-resolution 3DVAR analyses and ARW-WRF forecasts

This work looks at the impact on high-resolution analyses and forecasts of several non-conventional data types available within the Dallas Fort Worth (DFW) Urban Testbed. A major focus is an evaluation of the added value of the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) X-band radar network in the DFW area. The impact of this radar data is compared to that of the other radar networks in the area including the Next-Generation (WSR-88D NEXRAD) radars and the Terminal Doppler Weather Radars (TDWRs). Data denial experiments are performed using the Advanced Regional Prediction System (ARPS) Three-Dimensional Variational (3DVAR) analysis system and the Advanced Research Version of the Weather Research and Forecasting Model (ARW-WRF). Cycled data assimilation is performed on a 1 km grid with subsequent forecast performed on a 400 m grid. The case chosen is the 26 December 2015 tornado outbreak in north-central Texas with specific focus on model simulations of the Rowlett tornadic supercell. Verification results and comparison among data denial experiments show that the WSR-88D radar network appears to supply the most important data for this case. Forecasts of reflectivity (verified via fractions skill score (FSS)) and low-level rotation (0-1 km updraft helicity tracks verified via object-based track error algorithm) were able to accurately capture the storm evolution but only when the WSR-88D radar data was included. The Rowlett storm is on the edge of the shorter-range CASA and TDWR networks and so these radars are not able to provide sufficient observations to initialize the storm properly in the model. A noticeable increase in rotational intensity in the forecasted storm, possibly to unrealistic values, is found when CASA radar data is denied from the control experiment. Comparison of model quantitative precipitation forecast (QPF) output with observed rainfall estimates indicate a wet bias in the model used here which makes the precipitation forecasts less useful than the rotational forecasts. A separate experiment focus on surface observation impact found substantial positive impact of non-conventional surface observations on frontal placement southwest of the DFW metropolitan area

Understanding and predicting microbursts

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 1990.Vita.Includes bibliographical references (p. 281-300).by Marilyn Mitchell Wolfson.Ph.D

Impacts of Land Use and Land Cover on Remote Sensing Analyses of Thunderstorms and Their Attendant Hazards

Due to their hazardous nature, most thunderstorm observations today come from remote sensing platforms such as radar, satellite, and lightning detection sensors. Advancements in these sensor networks provide the ability to identify and track thunderstorms at finer spatial and temporal scales than ever before. Thunderstorms, however, are products of interactions between the land and atmosphere with certain land use and land cover (LULC) types augmenting the frequency and intensity of thunderstorms. Yet, these LULC effects may not be directly apparent when examining the remote sensing fields in isolation. This dissertation represents three research endeavors, each containing a multi-year climatology of a unique remote sensing dataset, to examine how the addition of LULC information affects the identification of thunderstorms and their attendant hazards and the interpretation of remote sensing products. First, a 20-year climatology of cloud-to-ground (CG) lightning data at 500 m spatial resolution quantifies an increase in isolated regions of high CG lightning frequencies in concert with the construction of antenna towers to accommodate the expansion of broadcasting and telecommunications technologies across the United States. CG lightning occurrence is correlated with antenna height and 96% of towers examined had a higher lightning density with 1 km of a tower compared to 2 km to 5 km away. Comparing tower strikes in the northern Great Plains reveals that shorter towers are more likely to observe larger CG lightning densities in the meteorological winter/fall months compared to the spring/summer months. Second, a five-year multi-radar/multi-sensor retrospective examining the effects of city size on thunderstorm initiation and longevity reveals an increase in thunderstorm frequency downwind of both cities larger than 1100 km^2 on convective days with ingredients historically shown to be conducive for urban-enhancement (i.e., summer months, afternoon initiation, synoptically weak days, non-supercell modes). As a result, downwind regions experienced a higher frequency of more intense composite reflectivity, vertically integrated liquid, and maximum expected size of hail values compared to equivalent distances upwind. Such effects were not observed in the smaller two cities and were not observed in any of the cities when examining the full five-year dataset. Finally, Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus surface reflectance data were acquired to examine how tornadoes alter the spectral behavior of grassland, forest, and urban land cover. Generally, independent of land cover type, tornadoes tend to increase the surface reflectance in the visible and shortwave-infrared spectral bands and decrease surface reflectance as measured within the near-infrared (NIR) spectral band. This results in a higher tasseled cap brightness, lower tasseled cap greenness and wetness, and a lower Normalized Difference Vegetation Index (NDVI) values. With tornado damaged forests providing an analogous signature to forest clearing, a five-year climatology of Landsat imagery was acquired to compare NDVI, a common damage identification metric derived from the red and NIR spectral bands, to a disturbance index (DI) derived from the tasseled cap indices to examine tornado damage in forests. DI is more resilient to seasonal variability as its coefficients are derived on an image-by-image basis, making it an optimal metric for a pixel-based identification and tracking of damage and subsequent recovery

Airborne Wind Shear Detection and Warning Systems: First Combined Manufacturers' and Technologists' Conference

The purpose of the meeting was to transfer significant, ongoing results gained during the first year of the joint NASA/FAA Airborne Wind Shear Program to the technical industry and to pose problems of current concern to the combined group. It also provided a forum for manufacturers to review forward-looking technology concepts and for technologists to gain an understanding of FAA certification requirements and the problems encountered by the manufacturers during the development of airborne equipment

ASSIMILATION OF ATTENUATED DATA FROM X-BAND NETWORK RADARS USING ENSEMBLE KALMAN FILTER

To use reflectivity data from X-band radars for quantitative precipitation estimation and storm-scale data assimilation, the effect of attenuation must be properly accounted for. Traditional approaches try to make correction to the attenuated reflectivity first before using the data. An alternative, theoretically more attractive approach builds the attenuation effect into the reflectivity observation operator of a data assimilation system, such as an ensemble Kalman filter (EnKF), allowing direct assimilation of the attenuated reflectivity and taking advantage of microphysical state estimation using EnKF methods for a potentially more accurate solution.This study first tests the approach for the CASA (Center for Collaborative Adaptive Sensing of the Atmosphere) X-band radar network configuration through observing system simulation experiments (OSSE) for a quasi-linear convective system (QLCS) that has more significant attenuation than isolated storms. To avoid the problem of potentially giving too much weight to fully attenuated reflectivity, an analytical, echo-intensity-dependent model for the observation error (AEM) is developed and is found to improve the performance of the filter. By building the attenuation into the forward observation operator and combining it with the application of AEM, the assimilation of attenuated CASA observations is able to produce a reasonably accurate analysis of the QLCS inside CASA radar network coverage. Compared with foregoing assimilation of radar data with weak radar reflectivity or assimilating only radial velocity data, our method can suppress the growth of spurious echoes while obtaining a more accurate analysis in the terms of root-mean-square (RMS) error. Sensitivity experiments are designed to examine the effectiveness of AEM by introducing multiple sources of observation errors into the simulated observations. The performance of such an approach in the presence of resolution-induced model error is also evaluated and good results are obtained.The same EnKF framework with attenuation correction is used to test different possible configurations of 2 hypothetical radars added to the existing network of 4 CASA radars through OSSEs. Though plans to expand the CASA radar network did not materialize, such experiments can provide guidance in the site selection of future X-band or other short-wavelength radar networks, as well as examining the benefit of X-band radar networks that consist of a much larger number of radars. Two QLCSs with different propagation speeds are generated and serve as the truth for our OSSEs. Assimilation and forecast results are compared among the OSSEs, assimilating only X-band or short-wavelength radar data. Overall, radar networks with larger downstream spatial coverage tend to provide overall the best analyses and 1-hour forecasts. The best analyses and forecasts of convective scale structure, however, are obtained when Dual- or Multi-Doppler coverage is preferred, even at the expense of minor loss in spatial coverage.Built-in attenuation correction is then applied, for the first time, to a real case (the 24 May 2011 tornadic storm near Chickasha, Oklahoma), using data from the X-band CASA radars. The attenuation correction procedure is found to be very effective--the analyses obtained using attenuated data are better than those obtained using pre-corrected data when all the values of reflectivity observations are assimilated. The effectiveness of the procedure is further examined by comparing the deterministic and ensemble forecasts started from the analysis of each experiment. The deterministic forecast experiment results indicate that assimilating un-corrected observations directly actually retains some information that might be lost in the pre-corrected CASA observations by forecasting a longer-lasting trailing line, similar to that observed in WSR-88D data. In the ensemble forecasts, assimilating un-corrected observations directly, using our attenuation-correcting EnKF, results in a forecast with a more intense tornado track than the experiment that assimilates all values of pre-corrected CASA data.This work is the first to assimilate attenuated observations from a radar network in OSSEs, as well as the first attempt to directly assimilate real, uncorrected CASA data into a numerical weather prediction (NWP) model using EnKF

Towards the “Perfect” Weather Warning

This book is about making weather warnings more effective in saving lives, property, infrastructure and livelihoods, but the underlying theme of the book is partnership. The book represents the warning process as a pathway linking observations to weather forecasts to hazard forecasts to socio-economic impact forecasts to warning messages to the protective decision, via a set of five bridges that cross the divides between the relevant organisations and areas of expertise. Each bridge represents the communication, translation and interpretation of information as it passes from one area of expertise to another and ultimately to the decision maker, who may be a professional or a member of the public. The authors explore the partnerships upon which each bridge is built, assess the expertise and skills that each partner brings and the challenges of communication between them, and discuss the structures and methods of working that build effective partnerships. The book is ordered according to the “first mile” paradigm in which the decision maker comes first, and then the production chain through the warning and forecast to the observations is considered second. This approach emphasizes the importance of co-design and co-production throughout the warning process. The book is targeted at professionals and trainee professionals with a role in the warning chain, i.e. in weather services, emergency management agencies, disaster risk reduction agencies, risk management sections of infrastructure agencies. This is an open access book

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne