Testing and Comparing Precipitation Algorithms

An algorithm (Alg. 2) was created to calculate precipitation rates and to predict weather events using raw data from a weather event simulator for the duration of two months. The Alg. 2’s results from the simulation were compared to the results of the algorithm that currently produces the data for the Marshall Field Site Webplots (Alg. 1) using a Pierce Skill Score(PSS) as a performance comparison. The two algorithms were also compared using the precipitation accumulation data, acquired from the Tall Double Fence Intercomparison Reference(DFIR) shielded GEONOR gauge in the Marshall field site, 1 April – 30 April 2017. The precipitation rate results from the two algorithms were then compared visually for this data set. The data acquired from these two comparisons showed that the adjusted algorithm had a Pierce Skill Score for predicting weather events that was 8.99% higher than the Alg. 1, it was quicker at identifying weather events, and had a reliable and accurate precipitation rate detection, but had a higher false alarm rate

An object-based approach for verification of precipitation estimation

Verification has become an integral component in the development of precipitation algorithms used in satellite-based precipitation products and evaluation of numerical weather prediction models. A number of object-based verification methods have been developed to quantify the errors related to spatial patterns and placement of precipitation. In this study, an image processing technique known as watershed transformation, capable of detecting closely spaced, but separable precipitation areas, is adopted in the object-based approach. Several key attributes of the segmented precipitation objects are selected and interest values of those attributes are estimated based on the distance measurement of the estimated and reference images. An overall interest score is estimated from all the selected attributes and their interest values. The proposed object-based approach is implemented to validate satellite-based precipitation estimation against ground radar observations. The results indicate that the watershed segmentation technique is capable of separating the closely spaced local-scale precipitation areas. In addition, three verification metrics, including the object-based false alarm ratio, object-based missing ratio, and overall interest score, reveal the skill of precipitation estimates in depicting the spatial and geometric characteristics of the precipitation structure against observations

Advancing the remote sensing of precipitation

Satellite-based global precipitation data has addressed the limitations of rain gauges and weather radar systems in forecasting applications and for weather and climate studies. Inspite of this ability, a number of issues that require the development of advanced concepts to address key challenges in satellite-based observations of precipitation were identified during the Advanced Concepts Workshop on Remote Sensing of Precipitation at Multiple Scales at the University of California. These include quantification of uncertainties of individual sensors and their propagation into multisensor products warrants a great deal of research. The development of metrics for validation and uncertainty analysis are of great importance. Bias removal, particularly probability distribution function (PDF)-based adjustment, deserves more in-depth research. Development of a near-real-time probabilistic uncertainty model for satellitebased precipitation estimates is highly desirable

Satellite-based precipitation estimation using watershed segmentation and growing hierarchical self-organizing map

This paper outlines the development of a multi-satellite precipitation estimation methodology that draws on techniques from machine learning and morphology to produce high-resolution, short-duration rainfall estimates in an automated fashion. First, cloud systems are identified from geostationary infrared imagery using morphology based watershed segmentation algorithm. Second, a novel pattern recognition technique, growing hierarchical self-organizing map (GHSOM), is used to classify clouds into a number of clusters with hierarchical architecture. Finally, each cloud cluster is associated with co-registered passive microwave rainfall observations through a cumulative histogram matching approach. The network was initially trained using remotely sensed geostationary infrared satellite imagery and hourly ground-radar data in lieu of a dense constellation of polar-orbiting spacecraft such as the proposed global precipitation measurement (GPM) mission. Ground-radar and gauge rainfall measurements were used to evaluate this technique for both warm (June 2004) and cold seasons (December 2004-February 2005) at various temporal (daily and monthly) and spatial (0.04 and 0.25) scales. Significant improvements of estimation accuracy are found classifying the clouds into hierarchical sub-layers rather than a single layer. Furthermore, 2-year (2003-2004) satellite rainfall estimates generated by the current algorithm were compared with gauge-corrected Stage IV radar rainfall at various time scales over continental United States. This study demonstrates the usefulness of the watershed segmentation and the GHSOM in satellite-based rainfall estimations

OLS data system/global survey of lightning

A global lightning climatology is being assembled from the nighttime imagery of the DMSP Optical Linescan Sensor (OLS). Lightning saturates the visible channel of the OLS at nighttime and can be identified as a horizontal streak on the order of 50-100 km in horizontal extent. Lightning streaks apparent in the film strips located at the National Snow and Ice Data Center (NSIDC) prior to 1991 are being digitized. An initial survey was completed for the F7 satellite observation period January 1986 - October 1987 and for the Q satellite for the period June-July 1973. Comparisons between the OLS lightning climatology with the Arkin GPI data set during the 1986-87 El Nino event shows similar regional variations in convective activity. The digital archive of global DMSP data began at the end of February. Software is being developed at both MSFC and NSIDC to extract, navigate, and view the OLS fine and smooth imagery

Evaluation and comparison of satellite precipitation estimates with reference to a local area in the Mediterranean Sea

Precipitation is one of the major variables for many applications and disciplines related to water resources and the geophysical Earth system. Satellite retrieval systems, rain-gauge networks, and radar systems are complementary to each other in terms of their coverage and capability of monitoring precipitation. Satellite-rainfall estimate systems produce data with global coverage that can provide information in areas for which data from other sources are unavailable. Without referring to ground measurements, satellite-based estimates can be biased and, although some gauge-adjusted satellite-precipitation products have been already developed, an effective way of integrating multi-sources of precipitation information is still a challenge.In this study, a specific area, the Sicilia Island (Italy), has been selected for the evaluation of satellite-precipitation products based on rain-gauge data. This island is located in the Mediterranean Sea, with a particular climatology and morphology, which can be considered an interesting test site for satellite-precipitation products in the European mid-latitude area. Four satellite products (CMORPH, PERSIANN, PERSIANN-CCS, and TMPA-RT) and two GPCP-adjusted products (TMPA and PERSIANN Adjusted) have been selected. Evaluation and comparison of selected products is performed with reference to data provided by the rain-gauge network of the Island Sicilia and by using statistical and graphical tools. Particular attention is paid to bias issues shown both by only-satellite and adjusted products. In order to investigate the current and potential possibilities of improving estimates by means of adjustment procedures using GPCC ground precipitation, the data have been retrieved separately and compared directly with the reference rain-gauge network data set of the study area.Results show that bias is still considerable for all satellite products, then some considerations about larger area climatology, PMW-retrieval algorithms, and GPCC data are discussed to address this issue, along with the spatial and seasonal characterization of results. © 2013 Elsevier B.V

ER-2 investigations of lightning and thunderstorms

The primary objective of the ER-2 lightning program is to investigate relationships between lightning and storm electrification and a number of underlying and interrelated phenomena including the structure, dynamics, and evolution of thunderstorms and thunderstorm systems, precipitation distribution and amounts, atmospheric chemistry processes, and the global electric circuit. This research is motivated by the desire to develop an understanding needed for the effective utilization and interpretation of data from the Lighting Imaging Sensor (LIS), the Lightning Mapper Sensor (LMS), and other satellite-based lightning detectors planned for the late 1900's and early 2000's. These satellite lightning detection systems will be characterized by high detection efficiencies (i.e., 90 percent) and the capability to detect both intracloud and cloud-to-ground discharges during day and night. The Lightning Imaging Sensor (LIS) is being developed by NASA for the Tropical Rainfall Measuring Mission (TRMM) satellite. In the ER-2 and related investigations, the emphasis is on establishing quantitative relationships and developing practical algorithms that employ lightning data, such as could be derived from satellite observations of optical lightning emissions, as the independent variable. Significant accomplishments made during the past year are presented

NWC SAF GEO Precipitation Products: Present Status and Future Developments

Presentación al congreso NWCSAF 2015 Users Workshop, 24-26 February 201

Evaluating several satellite precipitation estimates and global ground-based dataset on Sicily (Italy)

The developing of satellite-based precipitation retrieval systems, presents great potentialities for several applications ranging from weather and meteorological applications to hydrological modelling. Evaluating performances for these estimates is essential in order to understand their real capabilities and suitability related to each application. In this study an evaluation analysis of satellite precipitation retrieval systems has been carried out for the area of Sicily (Italy). Sicily is an island in the Mediterranean sea with a particular climatology and morphology, which is considered as an interesting test site for satellite precipitation products on the European mid-latitude area. A high density rain-gauges network has been used to evaluate selected satellite precipitation products. Sicily has an area of 26,000 km2 and the gauge density of the network considered in this study is about 250 km2/gauge. Four satellite products (CMORPH, PERSIANN, TMPA-RT, PERSIANN-CCS) along with two adjusted products (TMPA and PERSIANN Adjusted) have been selected for the evaluation. Evaluation and comparisons among selected products is performed with reference to the data provided by the gauge network of Sicily and using statistical and visualization tools. Results show that bias is relevant for all satellite products and climatic considerations are reported to address this issue. Moreover bias errors are observed for the adjusted products even though they are reduced respect to only-satellite products. In order to analyze this result, the ground-based precipitation dataset used by adjusted products (GPCC dataset), has been examined and weaknesses arising from spatial sampling of precipitation process have been identified for the study area. Therefore possible issues deriving from using global ground-based datasets for local scales are pointed out from this application. © 2012 SPIE