Li Z.,Tsinghua University |
Yang D.,Tsinghua University |
Hong Y.,University of Oklahoma |
Hong Y.,Advanced Radar Research Center
Journal of Hydrology | Year: 2013
In the present study, four high-resolution multi-sensor blended precipitation products, TRMM Multisatellite Precipitation Analysis (TMPA) research product (3B42 V7) and near real-time product (3B42 RT), Climate Prediction Center MORPHing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), are evaluated over the Yangtze River basin from April 2008 to March 2012 using the gauge data. This regional evaluation is performed at temporal scales ranging from annual to daily, based on a number of diagnostic statistics. Gauge adjustment greatly reduces the bias in 3B42 V7, a post real-time research product. Additionally, it helps the product maintain a stable skill level in winter. When additional indicators such as spatial correlation, Root Mean Square Error (RMSE), and Probability of Detection (POD) are considered, 3B42 V7 is not always superior to other products (especially CMORPH) at the daily scale. Among the near real-time datasets, 3B42 RT overestimates annual rainfall over the basin; CMORPH and PERSIANN underestimate it. In particular, the upper Yangtze always suffers from positive bias (>1mmday-1) in the 3B42 RT dataset and negative bias (-0.2 to -1mmday-1) in the CMORPH dataset. When seasonal scales are considered, CMORPH exhibits negative bias, mainly introduced during cold periods. The correlation between CMORPH and gauge data is the highest. On the contrary, the correlation between 3B42 RT and gauge data is more scattered; statistically, this results in lower bias. Finally, investigation of the probability distribution functions (PDFs) suggests that 3B42 V7 and 3B42 RT are consistently better at retrieving the PDFs in high-intensity events. Overall, this study provides useful information about the error characteristics associated with the four mainstream satellite precipitation products and their implications regarding hydrological applications over the Yangtze River basin. © 2013 Elsevier B.V.
Angrisani L.,University of Naples Federico II |
Petri D.,University of Trento |
Yeary M.,Advanced Radar Research Center
IEEE Instrumentation and Measurement Magazine | Year: 2015
The demand for ubiquitous connectivity is challenging the physical constraints placed upon current communication systems. In addition, customers expect higher and higher quality from their service providers. Consequently, equipment manufacturers are required to produce systems that can be quickly deployed and provide bandwidth-efficient communications. To meet this goal, instrumentation and measurements play a fundamental and invaluable role. © 1998-2012 IEEE.
The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter
Putnam B.J.,University of Oklahoma |
Xue M.,Center for Analysis and Predication of Storms |
Xue M.,Advanced Radar Research Center |
Jung Y.,Center for Analysis and Predication of Storms |
And 2 more authors.
Monthly Weather Review | Year: 2014
Doppler radar data are assimilated with an ensemble Kalman Filter (EnKF) in combination with a doublemoment (DM) microphysics scheme in order to improve the analysis and forecast of microphysical states and precipitation structures within a mesoscale convective system (MCS) that passed over western Oklahoma on 8-9 May 2007. Reflectivity and radial velocity data from five operational Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars as well as four experimental Collaborative and Adaptive Sensing of the Atmosphere (CASA) X-band radars are assimilated over a 1-h period using either single-moment (SM) or DM microphysics schemes within the forecast ensemble. Three-hour deterministic forecasts are initialized from the final ensemble mean analyses using a SMor DM scheme, respectively. Polarimetric radar variables are simulated from the analyses and compared with polarimetric WSR-88D observations for verification. EnKF assimilation of radar data using a multimoment microphysics scheme for an MCS case has not previously been documented in the literature. The use of DM microphysics during data assimilation improves simulated polarimetric variables through differentiation of particle size distributions (PSDs) within the stratiform and convective regions. The DM forecast initiated from the DM analysis shows significant qualitative improvement over the assimilation and forecast using SM microphysics in terms of the location and structure of the MCS precipitation. Quantitative precipitation forecasting skills are also improved in the DM forecast. Better handling of the PSDs by the DM scheme is believed to be responsible for the improved prediction of the surface cold pool, a stronger leading convective line, and improved areal extent of stratiform precipitation. © 2014 American Meteorological Society.
Wireless technology dangerously clutters the airwaves that meteorologists rely on to monitor thunderstorms, hurricanes and tornadoes, blacking out large swaths of weather radar maps. Wi-Fi, remote surveillance cameras and other wireless tech emit radio waves that can disrupt those from weather radars. This interference, which creates blind spots on radar images, is a growing problem, meteorologists report October 14 in the Bulletin of the American Meteorological Society. “Interference could hide an approaching tornado or a strong convective system and we wouldn’t have any warning,” says coauthor Elena Saltikoff, a meteorologist at the Finnish Meteorological Institute in Helsinki. Weather radar dishes blast radio waves that ricochet off water droplets in the air. Measuring these echoes allows meteorologists to monitor weather conditions up to hundreds of kilometers away. The returning radio waves can be less than a quintillionth the strength of the original signal, though, making the system vulnerable to devices that emit radio waves on similar frequencies. This disruption looks like blotches and streaks on radar images. While software can remove interference, it often can’t salvage the underlying weather data. Interference has been a meteorological nuisance for decades, but the problem has grown stratospherically, says study coauthor John Cho, an atmospheric radar scientist at the MIT Lincoln Laboratory in Lexington, Mass. In Europe, reports of wireless devices interfering with weather radars went from zero before 2006 to more than 200 in 2012. These incidents largely involved equipment such as Wi-Fi routers that had been hacked to circumvent built-in safeguards meant to reduce interference. In South Africa, interference became so bad that meteorologists switched radar frequencies, a move that cost millions of dollars in new equipment. Even after the switch, operators say they still battle rising interference. “We have to protect these frequencies; otherwise, forecasts and observations of storms will suffer,” says Robert Palmer, a radar meteorologist at the University of Oklahoma’s Advanced Radar Research Center in Norman.
Qiao L.,University of Oklahoma |
Qiao L.,Advanced Radar Research Center |
Qiao L.,Oklahoma State University |
Hong Y.,University of Oklahoma |
And 7 more authors.
Journal of Hydrology | Year: 2014
This study assesses the latest version, Version 7 (V7) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) rainfall estimates by comparison with the previous version, Version 6 (V6), for both near-real-time product (3B42RT) and post-real-time research products (3B42) over the climate-transitional zone in the southern Great Plains, USA. Two basins, the Verdigris River Basin (VRB) in the east and the Upper Washita Basin (UWB) in the west, with distinctive precipitation but similar vegetation and elevation, were selected to evaluate the TMPA products using rain gauge-blended products with WSR-88D NEXRAD Stage IV. This study sheds important insights into the detailed spatiotemporal precipitation errors, and also reveals algorithm performance during extreme events over the two low-relief basins within a high precipitation gradient zone. Based on nine years of measurements (2002-2010), this study shows that: (1) 3B42V7 corrects the widespread rainfall underestimation from research product 3B42V6, especially for the drier UWB with relative bias (RB) improvement from -23.24% to 2.24%. (2) 3B42RTV7 reduces the widespread, notable overestimation from the real-time product 3B42RTV6, with minor overestimation in the wet VRB and underestimation in the dry UWB. (3) For both versions of TMPA products, larger root mean square error (RMSE) but higher correlation coefficients (CCs) tend to appear for the wet VRB, while lower RMSE and CC mostly occur in the dry UWB. 3B42RTV7 shows a drawback that the CC declines significantly, especially in the dry region where it drops below 0.5. (4) Seasonally, autumn rainfall estimations in both versions and basins have the least bias. The 3B42RTV6 overestimation and 3B42V6 underestimation of spring and summer rainfall, which dominate the annual total bias, are significantly reduced for both basins in the V7 products. Winter precipitation estimation improvement is also noticeable with significant RB and RMSE reductions. However, considerable overestimation in summer rainfall still exists for the wet basin. (5) Although V7 has the overall best performance, it still shows deficiency in detecting extreme rainfall events in low-relief regions, tending to underestimate peak rainfall intensity and to misrepresent timing and locations. Results from this study can be used for reference in the algorithm development of the next generation of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) scheduled to launch in 2014. © 2014 Elsevier B.V.