A Comparison of Precipitation Estimation Techniques over Lake Okeechobee, Florida

10.1175/824.1 ◽  
2004 ◽  
Vol 19 (6) ◽  
pp. 1029-1043 ◽  
Author(s):  
Jamie L. Dyer ◽  
Reggina Cabrera Garza
Keyword(s):  
South Florida ◽  
Spatial Sampling ◽  
Stage Iii ◽  
Lake Okeechobee ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Hydrologic System ◽  
Data Source ◽  
Southern Florida ◽  
Relative Differences

Abstract Lake Okeechobee, located in southern Florida, is an important component in the regional hydrologic system. Currently, the Southeast River Forecast Center (SERFC) is setting up a forecasting scheme for Lake Okeechobee and its major inflows. An important aspect in calibrating the system is estimating the depth of direct precipitation over the water surface. Within this project, National Weather Service (NWS) and South Florida Water Management District (SFWMD) surface gauges, along with stage III multisensor precipitation estimates, are used to create time series of mean areal precipitation (MAP). The computed MAP values are compared in order to find the relative differences between them, and to determine the utility of using each data source for calibration and in future operations. It was found that the SFWMD gauge-based MAP was the most useful data source, because it had a suitable period of record and the SFWMD gauges had a better spatial sampling of precipitation over the lake surface. The radar-based stage III estimates were not found to be a useful source of data, despite the superior spatial sampling resolution, because they had too short a period of record and a number of changes in the processing algorithms made the associated MAP nonhomogeneous and inappropriate for model calibration.

Agricultural best management practices and surface water improvement and management

1995 ◽  
Vol 31 (8) ◽  
pp. 109-121 ◽  
Author(s):  
D. L. Anderson ◽  
E. G. Flaig
Keyword(s):  
Water Management ◽  
Surface Water ◽  
Best Management Practices ◽  
Management Practices ◽  
Agricultural Runoff ◽  
South Florida ◽  
Lake Okeechobee ◽  
Runoff Water ◽  
Best Management ◽  
Management Plans

Restoration and enhancement of Lake Okeechobee and the Florida Everglades requires a comprehensive approach to manage agricultural runoff. The Florida Surface Water Improvement and Management (SWIM) Act of 1987 was promulgated to develop and implement plans for protecting Florida waters. The South Florida Water Management District was directed by Florida legislature to develop management plans for Lake Okeechobee (SWIM) and the Everglades ecosystem (Marjory Stoneman Douglas Everglades Protection Act of 1991). These plans require agriculture to implement best management practices (BMPs) to reduce runoff phosphorus (P) loads. The Lake Okeechobee SWIM plan established a P load reduction target for Lake Okeechobee and set P concentration limitations for runoff from non-point source agricultural sources. Agricultural water users in the Everglades Agricultural Area (EAA) are required to develop farm management plans to reduce P loads from the basin by 25%. The Everglades Forever Act of 1994 additionally emphasized linkage of these landscapes and consequent protection and restoration of the Everglades. Agricultural BMPs are being developed and implemented to comply with water management, environmental, and regulatory standards. Although BMPs are improving runoff water quality, additional research is necessary to obtain the best combination of BMPs for individual farms. This paper summarizes the development of comprehensive water management in south Florida and the agricultural BMPs carried out to meet regulatory requirements for Lake Okeechobee and the Everglades.

A Performance-Based Regulatory Program for Phosphorus Control to Prevent the Accelerated Eutrophication of Lake Okeechobee, Florida

1993 ◽  
Vol 28 (3-5) ◽  
pp. 13-26 ◽  
Author(s):  
Alan L. Goldstein ◽  
Gary J. Ritter
Keyword(s):  
Water Management ◽  
Field Studies ◽  
South Florida ◽  
Lake Okeechobee ◽  
Cost Share ◽  
Scientific Application ◽  
Ongoing Research ◽  
Positive Effects ◽  
Regulatory Program ◽  
A Performance

Following 15 years of data collection, field studies, and modeling efforts, the State of Florida in 1987 legislatively mandated the South Florida Water Management District, a regional water management agency, to create and implement a plan to reduce average annual inputs of total phosphorus to Lake Okeechobee by 40 percent. One element of the resulting plan was the creation and implementation of a performance-based regulatory program that set phosphorus discharge limitations for all parcels of land equal to or greater than 1/2 acre in size in almost all of the 1,735,000 acres of the lake's 31 tributary drainage basins. Owners of non-complying parcels are required to take measures to bring the parcels into compliance. This regulatory program, coupled with concurrent cost-share incentive programs and ongoing research efforts, has resulted in a decrease in phosphorus concentrations from individual properties and at some tributary discharge locations to the lake. This effort demonstrates that where there is sufficient historical information, scientific application of state-of-the-art modeling techniques, a political will, and appropriate powers vested in the institutions to take and enforce actions, such programs can be implemented and have positive effects on reducing non-point source pollutants.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

scholarly journals Validation of CHIRPS Precipitation Estimates over Taiwan at Multiple Timescales

2021 ◽  
Vol 13 (2) ◽  
pp. 254 ◽  
Author(s):  
Jie Hsu ◽  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Xiuzhen Li
Keyword(s):  
Annual Cycle ◽  
Interannual Variation ◽  
Winter Precipitation ◽  
Monthly Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Precipitation Variation ◽  
Global Precipitation ◽  
Multiple Timescale ◽  
Better Than

The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which incorporates satellite imagery and in situ station information, is a new high-resolution long-term precipitation dataset available since 1981. This study aims to understand the performance of the latest version of CHIRPS in depicting the multiple timescale precipitation variation over Taiwan. The analysis is focused on examining whether CHIRPS is better than another satellite precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) final run (hereafter IMERG)—which is known to effectively capture the precipitation variation over Taiwan. We carried out the evaluations made for annual cycle, seasonal cycle, interannual variation, and daily variation during 2001–2019. Our results show that IMERG is slightly better than CHIRPS considering most of the features examined; however, CHIRPS performs better than that of IMERG in representing the (1) magnitude of the annual cycle of monthly precipitation climatology, (2) spatial distribution of the seasonal mean precipitation for all four seasons, (3) quantitative precipitation estimation of the interannual variation of area-averaged winter precipitation in Taiwan, and (4) occurrence frequency of the non-rainy grids in winter. Notably, despite the fact that CHIRPS is not better than IMERG for many examined features, CHIRPS can depict the temporal variation in precipitation over Taiwan on annual, seasonal, and interannual timescales with 95% significance. This highlights the potential use of CHIRPS in studying the multiple timescale variation in precipitation over Taiwan during the years 1981–2000, for which there are no data available in the IMERG database.

scholarly journals Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

2019 ◽  
Vol 20 (12) ◽  
pp. 2347-2365 ◽  
Author(s):  
Ali Jozaghi ◽  
Mohammad Nabatian ◽  
Seongjin Noh ◽  
Dong-Jun Seo ◽  
Lin Tang ◽  
...  
Keyword(s):  
Computational Cost ◽  
Mean Field ◽  
Remotely Sensed ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Radar Satellite ◽  
Quantitative Precipitation Estimates

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

scholarly journals PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

2020 ◽  
Vol 21 (12) ◽  
pp. 2893-2906
Author(s):  
Phu Nguyen ◽  
Mohammed Ombadi ◽  
Vesta Afzali Gorooh ◽  
Eric J. Shearer ◽  
Mojtaba Sadeghi ◽  
...  
Keyword(s):  
The Netherlands ◽  
Real Time ◽  
Rain Rate ◽  
Web Based ◽  
Precipitation Patterns ◽  
Diurnal Cycles ◽  
Critical Success ◽  
Extensive Evaluation ◽  
Precipitation Estimation ◽  
Precipitation Estimates

AbstractThis study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

scholarly journals Validation of GOME-2/Metop total column water vapour with ground-based and in situ measurements

2016 ◽  
Vol 9 (4) ◽  
pp. 1533-1544 ◽  
Author(s):  
Niilo Kalakoski ◽  
Jukka Kujanpää ◽  
Viktoria Sofieva ◽  
Johanna Tamminen ◽  
Margherita Grossi ◽  
...  
Keyword(s):  
Water Vapour ◽  
Atmospheric Chemistry ◽  
Relative Difference ◽  
Radiosonde Data ◽  
Climatic Data ◽  
National Climatic Data Center ◽  
Data Processor ◽  
Data Source ◽  
Relative Differences ◽  
Total Column

Abstract. The total column water vapour product from the Global Ozone Monitoring Experiment-2 on board Metop-A and Metop-B satellites (GOME-2/Metop-A and GOME-2/Metop-B) produced by the Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M SAF) is compared with co-located radiosonde observations and global positioning system (GPS) retrievals. The validation is performed using recently reprocessed data by the GOME Data Processor (GDP) version 4.7. The time periods for the validation are January 2007–July 2013 (GOME-2A) and December 2012–July 2013 (GOME-2B). The radiosonde data are from the Integrated Global Radiosonde Archive (IGRA) maintained by the National Climatic Data Center (NCDC). The ground-based GPS observations from the COSMIC/SuomiNet network are used as the second independent data source. We find a good general agreement between the GOME-2 and the radiosonde/GPS data. The median relative difference of GOME-2 to the radiosonde observations is −2.7 % for GOME-2A and −0.3 % for GOME-2B. Against the GPS, the median relative differences are 4.9 % and 3.2 % for GOME-2A and B, respectively. For water vapour total columns below 10 kg m−2, large wet biases are observed, especially against the GPS retrievals. Conversely, at values above 50 kg m−2, GOME-2 generally underestimates both ground-based observations.

scholarly journals Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

2019 ◽  
Vol 11 (23) ◽  
pp. 2741 ◽  
Author(s):  
Aminyavari ◽  
Saghafian ◽  
Sharifi
Keyword(s):  
Weather Prediction ◽  
Precipitation Amount ◽  
Early Warning Systems ◽  
Flood Impacts ◽  
Spatial Coverage ◽  
Basin Scale ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Precipitation Threshold ◽  
Precipitation Thresholds

Precipitation monitoring and early warning systems are required to reduce negative flood impacts. In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the THORPEX interactive grand global ensemble (TIGGE) as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG, for precipitation estimates were evaluated in recent severe floods in Iran over the March–April 2019 period. The evaluations were conducted in three aspects: spatial distribution of precipitation, mean areal precipitation in three major basins hard hit by the floods, and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations. Moreover, with regard to mean precipitation at the basin scale, UKMO and European Center for Medium-Range Weather Forecasts (ECMWF) models in the Gorganrud Basin, ECMWF in the Karkheh Basin and UKMO in the Karun Basin performed better than others in flood forecasting. The National Centers for Environmental Forecast (NCEP) model performed well at low precipitation thresholds, while at high thresholds, its performance decreased significantly. On the contrary, the accuracy of IMERG improved when the precipitation threshold increased. The UKMO had better forecasts than the other models at the 100 mm/day precipitation threshold, whereas the ECMWF had acceptable forecasts in all thresholds and was able to forecast precipitation events with a lower false alarm ratio and better detection when compared to other models.

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