scholarly journals Gridded Monthly Rainfall Estimates Derived from Historical Atoll Observations

2019 ◽  
Vol 36 (4) ◽  
pp. 671-687 ◽  
Author(s):  
Werner E. Cook ◽  
J. Scott Greene
Keyword(s):  
Ordinary Kriging ◽  
Global Precipitation Climatology Project ◽  
Monthly Rainfall ◽  
Standard Errors ◽  
Analysis Tool ◽  
University Of Oklahoma ◽  
Kriging Variance ◽  
The Pacific ◽  
Areal Rainfall ◽  
Rainfall Estimates

AbstractTo provide an analysis tool for areal rainfall estimates, 1° gridded monthly sea level rainfall estimates have been derived from historical atoll rainfall observations contained in the Pacific Rainfall (PACRAIN) database. The PACRAIN database is a searchable repository of in situ rainfall observations initiated and maintained by the University of Oklahoma and supported by a research grant from the National Oceanic and Atmospheric Administration (NOAA)/Climate Program Office/Ocean Observing and Monitoring. The gridding algorithm employs ordinary kriging, a standard geostatistical technique, and selects for nonnegative estimates and for local estimation neighborhoods yielding minimum kriging variance. This methodology facilitates the selection of fixed-size neighborhoods from available stations beyond simply choosing the closest stations, as it accounts for dependence between estimator stations. The number of stations used for estimation is based on bias and standard error exhibited under cross estimation. A cross validation is conducted, comparing estimated and observed rains, as well as theoretical and observed standard errors for the ordinary kriging estimator. The conditional bias of the kriging estimator and the predictive value of kriging standard errors, with respect to observed standard errors, are discussed. Plots of the gridded rainfall estimates are given for sample El Niño and La Niña cases and standardized differences between the estimates produced here and the merged monthly rainfall estimates published by the Global Precipitation Climatology Project (GPCP) are shown and discussed.

scholarly journals CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological use

2015 ◽  
Vol 8 (1) ◽  
pp. 83-112 ◽  
Author(s):  
V. D. J. Keller ◽  
M. Tanguy ◽  
I. Prosdocimi ◽  
J. A. Terry ◽  
O. Hitt ◽  
...  
Keyword(s):  
Monthly Rainfall ◽  
Annual Rainfall ◽  
National Database ◽  
Maximum Information ◽  
Weather Observations ◽  
The Republic ◽  
Areal Rainfall ◽  
The Uk ◽  
Rainfall Estimates

Abstract. The Centre for Ecology & Hydrology – Gridded Estimates of Areal Rainfall (CEH-GEAR) dataset was developed to provide reliable 1 km gridded estimates of daily and monthly rainfall for Great Britain (GB) and Northern Ireland (NI) (together with approximately 3500 km2 of catchment in the Republic of Ireland) from 1890 onwards. The dataset was primarily required to support hydrological modelling. The rainfall estimates are derived from the Met Office collated historical weather observations for the UK which include a national database of raingauge observations. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall, was used to generate the daily and monthly rainfall grids. To derive the monthly estimates, rainfall totals from monthly and daily (when complete month available) read raingauges were used in order to obtain maximum information from the raingauge network. The daily grids were adjusted so that the monthly grids are fully consistent with the daily grids. The CEH-GEAR dataset was developed according to the guidance provided by the British Standards Institution. The CEH-GEAR dataset contains 1 km grids of daily and monthly rainfall estimates for GB and NI for the period 1890–2012. For each day and month, CEH-GEAR includes a secondary grid of distance to the nearest operational raingauge. This may be used as an indicator of the quality of the estimates. When this distance is greater than 100 km, the estimates are not calculated due to high uncertainty. CEH-GEAR is available free of charge for commercial and non-commercial use subject to licensing terms and conditions. doi:10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e

scholarly journals Improved Global Rainfall Retrieval Using the Special Sensor Microwave Imager (SSM/I)

2010 ◽  
Vol 49 (5) ◽  
pp. 1032-1043 ◽  
Author(s):  
Daniel Vila ◽  
Ralph Ferraro ◽  
Hilawe Semunegus
Keyword(s):  
Rainfall Variability ◽  
Substantial Reduction ◽  
Global Precipitation Climatology Project ◽  
Global Scale ◽  
Monthly Rainfall ◽  
Daily Data ◽  
Interannual Rainfall Variability ◽  
Microwave Imager ◽  
Global Precipitation ◽  
Rainfall Estimates

Abstract Global monthly rainfall estimates have been produced from more than 20 years of measurements from the Defense Meteorological Satellite Program series of Special Sensor Microwave Imager (SSM/I). This is the longest passive microwave dataset available to analyze the seasonal, annual, and interannual rainfall variability on a global scale. The primary algorithm used in this study is an 85-GHz scattering-based algorithm over land, while a combined 85-GHz scattering and 19/37-GHz emission is used over ocean. The land portion of this algorithm is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. Because previous SSM/I processing was performed in real time, only a basic quality control (QC) procedure had been employed to avoid unrealistic values in the input data. A more sophisticated, statistical-based QC procedure on the daily data grids (antenna temperature) was developed to remove unrealistic values not detected in the original database and was employed to reprocess the rainfall product using the current version of the algorithm for the period 1992–2007. Discrepancies associated with the SSM/I-derived monthly rainfall products are characterized through comparisons with various gauge-based and other satellite-derived rainfall estimates. A substantial reduction in biases was observed as a result of this QC scheme. This will yield vastly improved global rainfall datasets.

scholarly journals CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological and other applications

2015 ◽  
Vol 7 (1) ◽  
pp. 143-155 ◽  
Author(s):  
V. D. J. Keller ◽  
M. Tanguy ◽  
I. Prosdocimi ◽  
J. A. Terry ◽  
O. Hitt ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Monthly Rainfall ◽  
Annual Rainfall ◽  
National Database ◽  
Data Set ◽  
Maximum Information ◽  
The Republic ◽  
Areal Rainfall ◽  
The Uk ◽  
Rainfall Estimates

Abstract. The Centre for Ecology & Hydrology – Gridded Estimates of Areal Rainfall (CEH-GEAR) data set was developed to provide reliable 1 km gridded estimates of daily and monthly rainfall for Great Britain (GB) and Northern Ireland (NI) (together with approximately 3500 km2 of catchment in the Republic of Ireland) from 1890 onwards. The data set was primarily required to support hydrological modelling. The rainfall estimates are derived from the Met Office collated historical weather observations for the UK which include a national database of rain gauge observations. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall (AAR), was used to generate the daily and monthly rainfall grids. To derive the monthly estimates, rainfall totals from monthly and daily (when complete month available) rain gauges were used in order to obtain maximum information from the rain gauge network. The daily grids were adjusted so that the monthly grids are fully consistent with the daily grids. The CEH-GEAR data set was developed according to the guidance provided by the British Standards Institution. The CEH-GEAR data set contains 1 km grids of daily and monthly rainfall estimates for GB and NI for the period 1890–2012. For each day and month, CEH-GEAR includes a secondary grid of distance to the nearest operational rain gauge. This may be used as an indicator of the quality of the estimates. When this distance is greater than 100 km, the estimates are not calculated due to high uncertainty. CEH-GEAR is available from doi:10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e and is free of charge for commercial and non-commercial use subject to licensing terms and conditions.

scholarly journals AREAL RAINFALL ESTIMATES

1929 ◽  
Vol 57 (11) ◽  
pp. 462-464 ◽  
Author(s):  
EDWARD N. WHITNEY
Keyword(s):  
Areal Rainfall ◽  
Rainfall Estimates

scholarly journals A New Parameter to Assess Impact of Rain Gauge Density on Uncertainty in the Estimate of Monthly Rainfall over India

2019 ◽  
Vol 20 (5) ◽  
pp. 821-832 ◽  
Author(s):  
Satya Prakash ◽  
Ashwin Seshadri ◽  
J. Srinivasan ◽  
D. S. Pai
Keyword(s):  
Theoretical Model ◽  
Central India ◽  
Ground Truth ◽  
Accurate Method ◽  
Rain Gauge ◽  
Monthly Rainfall ◽  
Spatial Density ◽  
Spatial Average ◽  
Rain Gauges ◽  
Rainfall Estimates

Abstract Rain gauges are considered the most accurate method to estimate rainfall and are used as the “ground truth” for a wide variety of applications. The spatial density of rain gauges varies substantially and hence influences the accuracy of gridded gauge-based rainfall products. The temporal changes in rain gauge density over a region introduce considerable biases in the historical trends in mean rainfall and its extremes. An estimate of uncertainty in gauge-based rainfall estimates associated with the nonuniform layout and placement pattern of the rain gauge network is vital for national decisions and policy planning in India, which considers a rather tight threshold of rainfall anomaly. This study examines uncertainty in the estimation of monthly mean monsoon rainfall due to variations in gauge density across India. Since not all rain gauges provide measurements perpetually, we consider the ensemble uncertainty in spatial average estimation owing to randomly leaving out rain gauges from the estimate. A recently developed theoretical model shows that the uncertainty in the spatially averaged rainfall is directly proportional to the spatial standard deviation and inversely proportional to the square root of the total number of available gauges. On this basis, a new parameter called the “averaging error factor” has been proposed that identifies the regions with large ensemble uncertainties. Comparison of the theoretical model with Monte Carlo simulations at a monthly time scale using rain gauge observations shows good agreement with each other at all-India and subregional scales. The uncertainty in monthly mean rainfall estimates due to omission of rain gauges is largest for northeast India (~4% uncertainty for omission of 10% gauges) and smallest for central India. Estimates of spatial average rainfall should always be accompanied by a measure of uncertainty, and this paper provides such a measure for gauge-based monthly rainfall estimates. This study can be further extended to determine the minimum number of rain gauges necessary for any given region to estimate rainfall at a certain level of uncertainty.

An invasive ant distribution database to support biosecurity risk analysis in the Pacific

2017 ◽  
Vol 23 (3) ◽  
pp. 258 ◽  
Author(s):  
Monica A. M. Gruber ◽  
Meghan Cooling ◽  
Allan R. Burne
Keyword(s):  
Invasive Species ◽  
Risk Analysis ◽  
Native Species ◽  
Small Island ◽  
Accurate Information ◽  
Analysis Tool ◽  
Invasive Ants ◽  
Invasive Ant ◽  
Conflicting Information ◽  
The Pacific

Invasive species are one of the most serious threats to biodiversity. Up-to-date and accurate information on the distribution of invasive species is an important biosecurity risk analysis tool. Several databases are available to determine the distributions of invasive and native species. However, keeping this information current is a real challenge. Ants are among the most widespread invasive species. Five species of ants are listed in the IUCN list of damaging invasive species, and many other species are also invasive in the Pacific. We sought to determine and update the distribution information for the 18 most problematic invasive ant species in the Pacific to assist Small Island Developing States with risk analysis. We compared the information on six public databases, conducted a literature review, and contacted experts on invasive ants in the Pacific region to resolve conflicting information. While most public records were accurate we found some new records had not yet been incorporated in the public databases, and some information was inaccurate. The maintenance of public databases faces an enormous challenge in balancing completeness (~15 000 ant species in this case) with accuracy (the impossibility of constantly surveying) and utility.

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