A Gauge-Based Analysis of Daily Precipitation over East Asia

2007 ◽  
Vol 8 (3) ◽  
pp. 607-626 ◽  
Author(s):  
Pingping Xie ◽  
Mingyue Chen ◽  
Song Yang ◽  
Akiyo Yatagai ◽  
Tadahiro Hayasaka ◽  
...  
Keyword(s):  
East Asia ◽  
Daily Precipitation ◽  
Interpolation Method ◽  
Optimal Interpolation ◽  
Network Density ◽  
Monthly Precipitation ◽  
Orographic Effects ◽  
Precipitation Estimates ◽  
The Mean ◽  
Validation Tests

Abstract A new gauge-based analysis of daily precipitation has been constructed on a 0.5° latitude–longitude grid over East Asia (5°–60°N, 65°–155°E) for a 26-yr period from 1978 to 2003 using gauge observations at over 2200 stations collected from several individual sources. First, analyzed fields of daily climatology are computed by interpolating station climatology defined as the summation of the first six harmonics of the 365-calendar-day time series of the mean daily values averaged over a 20-yr period from 1978 to 1997. These fields of daily climatology are then adjusted by the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) monthly precipitation climatology to correct the bias caused by orographic effects. Gridded fields of the ratio of daily precipitation to the daily climatology are created by interpolating the corresponding station values using the optimal interpolation method. Analyses of total daily precipitation are finally calculated by multiplying the daily climatology by the daily ratio. Cross-validation tests indicated that this gauge-based analysis has high quantitative quality with a negligible bias and a correlation coefficient of ∼0.6 for comparisons between withdrawn station data and the analysis at a 0.05° latitude–longitude grid box. The quality of the analysis increases with the gauge network density. The mean distribution and annual cycle of this new gauge analysis present similar patterns but with more detailed structures and slightly larger magnitude compared to other published monthly gauge analyses over the region. The East Asia gauge analysis is applied to verify the performance of five satellite-based precipitation estimates. This examination reveals the regionally and seasonally dependent performance of the satellite products with the best statistics observed for relatively wet regions. Further improvements of the daily gauge analysis are underway to increase the gauge network density and to refine the algorithm to better deal with the orographic effects especially over South and Southeast Asia.

scholarly journals Evaluation of Precipitation Estimation Accuracy in Reanalyses, Satellite Products, and an Ensemble Method for Regions in Australia and South and East Asia

2013 ◽  
Vol 14 (4) ◽  
pp. 1323-1333 ◽  
Author(s):  
Jorge L. Peña-Arancibia ◽  
Albert I. J. M. van Dijk ◽  
Luigi J. Renzullo ◽  
Mark Mulligan
Keyword(s):  
East Asia ◽  
Precipitation Amount ◽  
Department Of Energy ◽  
Estimation Accuracy ◽  
Monthly Precipitation ◽  
Precipitation Regime ◽  
Monsoon Period ◽  
Ensemble Mean ◽  
Precipitation Estimation ◽  
Precipitation Estimates

Abstract Precipitation estimates from reanalyses and satellite observations are routinely used in hydrologic applications, but their accuracy is seldom systematically evaluated. This study used high-resolution gauge-only daily precipitation analyses for Australia (SILO) and South and East Asia [Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE)] to calculate the daily detection and accuracy metrics for three reanalyses [ECMWF Re-Analysis Interim (ERA-Interim), Japanese 25-yr Reanalysis (JRA-25), and NCEP–Department of Energy (DOE) Global Reanalysis 2] and three satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) 3B42V6, Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)]. A depth-frequency-adjusted ensemble mean of the reanalyses and satellite products was also evaluated. Reanalyses precipitation from ERA-Interim in southern Australia (SAu) and northern Australasia (NAu) showed higher detection performance. JRA-25 had a better performance in South and East Asia (SEA) except for the monsoon period, in which satellite estimates from TRMM and CMORPH outperformed the reanalyses. In terms of accuracy metrics (correlation coefficient, root-mean-square difference, and a precipitation intensity proxy, which is the ratio of monthly precipitation amount to total days with precipitation) and over the three subdomains, the depth-frequency-adjusted ensemble mean generally outperformed or was nearly as good as any of the single members. The results of the ensemble show that additional information is captured from the different precipitation products. This finding suggests that, depending on precipitation regime and location, combining (re)analysis and satellite products can lead to better precipitation estimates and, thus, more accurate hydrological applications than selecting any single product.

scholarly journals Trends in Precipitation Extremes in the Zhujiang River Basin, South China

2011 ◽  
Vol 24 (3) ◽  
pp. 750-761 ◽  
Author(s):  
Marco Gemmer ◽  
Thomas Fischer ◽  
Tong Jiang ◽  
Buda Su ◽  
Lü Liu Liu
Keyword(s):  
River Basin ◽  
South China ◽  
Wind Direction ◽  
Daily Precipitation ◽  
Monthly Precipitation ◽  
Maximum Precipitation ◽  
Daily Precipitation Data ◽  
Precipitation Indices ◽  
The Mean ◽  
Zhujiang River

Abstract Spatial and temporal characteristics of precipitation trends in the Zhujiang River basin, South China, are analyzed. Nonparametric trend tests are applied to daily precipitation data from 192 weather stations between 1961 and 2007 for the following indices: annual, monthly, and daily precipitation; annual and monthly number of rain days and precipitation intensity; annual and monthly maximum precipitation; 5-day maximum precipitation, number of rainstorms with >50 mm day−1, and peaks over thresholds (90th, 95th, and 99th percentile). The results show that few stations experienced trends in the precipitation indices on an annual basis. On a monthly basis, significant positive and negative trends above the 90% confidence level appear in all months except December. Trends in the indices of monthly precipitation, rain intensity, rain days, and monthly maximum precipitation show very similar characteristics. They experience the most distinct negative (positive) trends in October (January). A change of the mean wind direction by 50° from east-southeast to east-northeast explains the downward trend in precipitation in October. Dry October months (months with low precipitation indices) can be observed when the mean wind direction is east-northeast (arid) instead of the prevailing mean wind direction, east-southeast (moist). The former is typical for the East Asian winter monsoon (EAWM). Nearly 90% of the driest October months can be explained by wind directions typical for the EAWM. The upward trend in precipitation indices in January cannot be explained by changes in large-scale circulation. The analysis of the precipitation indices delivers more detailed information on observed changes than other studies in the same area. This can be attributed to the higher station density, the quality of daily data, and the focus on monthly trends in the current study.

scholarly journals On the resolutions of ocean altimetry maps

Ocean Science ◽  
2019 ◽  
Vol 15 (4) ◽  
pp. 1091-1109 ◽  
Author(s):  
Maxime Ballarotta ◽  
Clément Ubelmann ◽  
Marie-Isabelle Pujol ◽  
Guillaume Taburet ◽  
Florent Fournier ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Interpolation Method ◽  
Tide Gauge ◽  
Optimal Interpolation ◽  
Signal Ratio ◽  
Spectral Content ◽  
Climate Forecasting ◽  
Ocean Altimetry ◽  
The Mean ◽  
Marine Environment Monitoring

Abstract. The Data Unification and Altimeter Combination System (DUACS) produces sea level global and regional maps that serve oceanographic applications, climate forecasting centers, and geophysics and biology communities. These maps are generated using an optimal interpolation method applied to altimeter observations. They are provided on a global 1∕4∘ × 1∕4∘ (longitude × latitude) and daily grid resolution framework (1∕8∘ × 1∕8∘ longitude × latitude grid for the regional products) through the Copernicus Marine Environment Monitoring Service (CMEMS). Yet, the dynamical content of these maps does not have full 1∕4∘ spatial and 1 d temporal resolutions due to the filtering properties of the optimal interpolation. In the present study, we estimate the effective spatial and temporal resolutions of the newly reprocessed delayed-time DUACS maps (a.k.a. DUACS-DT2018). Our approach is based on the ratio between the spectral content of the mapping error and the spectral content of independent true signals (along-track and tide gauge observations), also known as the noise-to-signal ratio. We found that the spatial resolution of the DUACS-DT2018 global maps based on sampling by three altimeters simultaneously ranges from ∼100 km wavelength at high latitude to ∼800 km wavelength in the equatorial band and the mean temporal resolution is ∼34 d. The mean effective spatial resolution at midlatitude is estimated to be ∼200 km. The mean effective spatial resolution is ∼130 km for the regional Mediterranean Sea and for the regional Black Sea products. An intercomparison with previous DUACS reprocessing systems (a.k.a., DUACS-DT2010 and DUACS-DT2014) highlights the progress of the system over the past 8 years, in particular a gain of resolution in highly turbulent regions. The same diagnostic applied to maps constructed with two altimeters and maps with three altimeters confirms a modest increase in resolving capabilities and accuracies in the DUACS maps with the number of missions.

scholarly journals Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 333
Author(s):  
Altemar L. Pedreira Junior ◽  
Marcelo S. Biudes ◽  
Nadja G. Machado ◽  
George L. Vourlitis ◽  
Hatim M. E. Geli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Agricultural Production ◽  
Daily Precipitation ◽  
Annual Precipitation ◽  
Monthly Precipitation ◽  
Mato Grosso ◽  
Significant Difference ◽  
Precipitation Estimates ◽  
Global Precipitation

The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management.

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 Numerical simulations of spreading of the Persian Gulf outflow into the Oman Sea

Ocean Science ◽  
2010 ◽  
Vol 6 (4) ◽  
pp. 887-900 ◽  
Author(s):  
M. Ezam ◽  
A. A. Bidokhti ◽  
A. H. Javid
Keyword(s):  
Persian Gulf ◽  
Bottom Topography ◽  
World Ocean ◽  
Monthly Precipitation ◽  
Boundary Current ◽  
Surface Salinity ◽  
Data Set ◽  
Open Boundaries ◽  
The Mean ◽  
The Persian Gulf

Abstract. A three dimensional numerical model namely POM (Princeton Ocean Model) and observational data are used to study the Persian Gulf outflow structure and its spreading pathways during 1992. In the model, the monthly wind speed data were taken from ICOADS (International Comprehensive Ocean-Atmosphere Data Set) and the monthly SST (sea surface temperatures) were taken from AVHRR (Advanced Very High Resolution Radiometer) with the addition of monthly net shortwave radiations from NCEP (National Center for Environmental Prediction). The mean monthly precipitation rates from NCEP data and the calculated evaporation rates are used to impose the surface salinity fluxes. At the open boundaries the temperature and salinity were prescribed from the mean monthly climatological values from WOA05 (World Ocean Atlas 2005). Also the four major components of the tide were prescribed at the open boundaries. The results show that the outflow mainly originates from two branches at different depths in the Persian Gulf. The permanent branch exists during the whole year deeper than 40 m along the Gulf axis and originates from the inner parts of the Persian Gulf. The other seasonal branch forms in the vicinity of the shallow southern coasts due to high evaporation rates during winter. Near the Strait of Hormuz the two branches join and form the main outflow source water. The results of simulations reveal that during the winter the outflow boundary current mainly detaches from the coast well before Ras Al Hamra Cape, however during summer the outflow seems to follow the coast even after this Cape. This is due to a higher density of the colder outflow that leads to more sinking near the coast in winter. Thus, the outflow moves to a deeper depth of about 500 m (for which some explanations are given) while the main part detaches and spreads at a depth of about 300 m. However in summer it all moves at a depth of about 200–250 m. During winter, the deeper, stronger and wider outflow is more affected by the steep topography, leading to separation from the coast. While during summer, the weaker and shallower outflow is less influenced by bottom topography and so continues along the boundary.

Research about Influencing Factor on Interpolation Accuracy in Data Exchange of Fluid-Structure Interaction

2013 ◽  
Vol 318 ◽  
pp. 100-107
Author(s):  
Zhen Shen ◽  
Biao Wang ◽  
Hui Yang ◽  
Yun Zheng
Keyword(s):  
Data Exchange ◽  
Shape Function ◽  
Interpolation Method ◽  
Matching Condition ◽  
Optimal Interpolation ◽  
Complex Interpolation ◽  
Interpolation Function ◽  
Interpolation Methods ◽  
Interpolation Accuracy ◽  
Lower Accuracy

Six kinds of interpolation methods, including projection-shape function method, three-dimensional linear interpolation method, optimal interpolation method, constant volume transformation method and so on, were adoped in the study of interpolation accuracy. From the point of view about the characterization of matching condition of two different grids and interpolation function, the infuencing factor on the interpolation accuracy was studied. The results revealed that different interpolation methods had different interpolation accuracy. The projection-shape function interpolation method had the best effect and the more complex interpolation function had lower accuracy. In many cases, the matching condition of two grids had much greater impact on the interpolation accuracy than the method itself. The error of interpolation method is inevitable, but the error caused by the grid quality could be reduced through efforts.

scholarly journals Mean Dynamic Topography Modeling Based on Optimal Interpolation from Satellite Gravimetry and Altimetry Data

2021 ◽  
Vol 11 (11) ◽  
pp. 5286
Author(s):  
Yihao Wu ◽  
Jia Huang ◽  
Hongkai Shi ◽  
Xiufeng He
Keyword(s):  
Ocean Circulation ◽  
Interpolation Method ◽  
Optimal Interpolation ◽  
Surface Model ◽  
Geopotential Model ◽  
Generation Model ◽  
Dynamic Topography ◽  
Altimetry Data ◽  
Mean Dynamic Topography ◽  
Gaussian Filtering

Mean dynamic topography (MDT) is crucial for research in oceanography and climatology. The optimal interpolation method (OIM) is applied to MDT modeling, where the error variance–covariance information of the observations is established. The global geopotential model (GGM) derived from GOCE (Gravity Field and Steady-State Ocean Circulation Explorer) gravity data and the mean sea surface model derived from satellite altimetry data are combined to construct MDT. Numerical experiments in the Kuroshio over Japan show that the use of recently released GOCE-derived GGM derives a better MDT compared to the previous models. The MDT solution computed based on the sixth-generation model illustrates a lower level of root mean square error (77.0 mm) compared with the ocean reanalysis data, which is 2.4 mm (5.4 mm) smaller than that derived from the fifth-generation (fourth-generation) model. This illustrates that the accumulation of GOCE data and updated data preprocessing methods can be beneficial for MDT recovery. Moreover, the results show that the OIM outperforms the Gaussian filtering approach, where the geostrophic velocity derived from the OIM method has a smaller misfit against the buoy data, by a magnitude of 10 mm/s (17 mm/s) when the zonal (meridional) component is validated. This is mainly due to the error information of input data being used in the optimal interpolation method, which may obtain more reasonable weights of observations than the Gaussian filtering method.

scholarly journals Systematic Variation in Wintertime Precipitation in East Asia by MJO-Induced Extratropical Vertical Motion

2008 ◽  
Vol 21 (4) ◽  
pp. 788-801 ◽  
Author(s):  
Jee-Hoon Jeong ◽  
Baek-Min Kim ◽  
Chang-Hoi Ho ◽  
Yeon-Hee Noh
Keyword(s):  
East Asia ◽  
Large Scale ◽  
Diabatic Heating ◽  
Daily Precipitation ◽  
Lower Troposphere ◽  
Vertical Motion ◽  
Mean Value ◽  
Asian Countries ◽  
The Indian Ocean ◽  
Omega Equation

Abstract The variations in the wintertime precipitation over East Asia and the related large-scale circulation associated with the Madden–Julian oscillation (MJO) are examined. By analyzing the observed daily precipitation for the period 1974–2000, it is found that the MJO significantly modulates the distribution of precipitation over four East Asian countries; the precipitation rate difference between wet and dry periods over East Asia, when the centers of MJO convective activities are located over the Indian Ocean and western Pacific, respectively, reaches 3–4 mm day−1, which corresponds to the climatological winter-mean value. Composite analysis with respect to the MJO suggests that the MJO–precipitation relation is mostly explained by the strong vertical motion anomalies near an entrance region of the East Asia upper-tropospheric jet and moisture supply in the lower troposphere. To elucidate different dynamic origins of the vertical motion generated by the MJO, diagnostic analysis of a generalized omega equation is adopted. It is revealed that about half of the vertical motion anomalies in East Asia are induced by the quasigeostrophic forcings by the MJO, while diabatic heating forcings explain a very small fraction, less than 10% of total anomalies.

scholarly journals A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

2021 ◽  
Vol 13 (11) ◽  
pp. 2040
Author(s):  
Xin Yan ◽  
Hua Chen ◽  
Bingru Tian ◽  
Sheng Sheng ◽  
Jinxing Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Resolution ◽  
Daily Precipitation ◽  
Machine Learning Algorithms ◽  
Precipitation Data ◽  
Large Area ◽  
Tree Model ◽  
Daily Precipitation Data ◽  
Precipitation Estimates

High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.

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