scholarly journals Evaluating the Drought-Monitoring Utility of Four Satellite-Based Quantitative Precipitation Estimation Products at Global Scale

2019 ◽  
Vol 11 (17) ◽  
pp. 2010 ◽  
Author(s):  
Haigen Zhao ◽  
Yanfei Ma
Keyword(s):  
Global Climate ◽  
Meteorological Drought ◽  
Global Scale ◽  
Probability Of Detection ◽  
Drought Monitoring ◽  
The Tibetan Plateau ◽  
Climate Data ◽  
Quantitative Precipitation Estimation ◽  
Southeastern Us ◽  
Precipitation Estimation

This study simultaneously analyzed and evaluated the meteorological drought-monitoring utility of the following four satellite-based, quantitative precipitation estimation (QPE) products: the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis 3B43V7 (TRMM-3B43), the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), the Climate Prediction Center Morphing Technique gauge-satellite blended product (CMORPH-BLD), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). Data from 2000 to 2016 was used at global scale. The global Climate Research Unit (CRU) Version 4.02 was used as reference data to assess QPE products. The Standardized Precipitation Evapotranspiration Index (SPEI) drought index was chosen as an example to evaluate the drought utility of four QPE products. The results indicate that CHIRPS has the best performance in Europe, Oceania, and Africa; the PERSIANN-CDR has the best performance in North America, South America, and Asia; the CMORPH-BLD has the worst statistical indices in all continents. Although four QPE products showed satisfactory performance for most of the world according to SPEI statistics, poor drought monitoring ability occurred in Southeast Asia, Central Africa, the Tibetan plateau, the Himalayas, and Amazonia. The PERSIANN-CDR achieves the best performance of the four QPE products in most regions except for Africa; CHIRPS and TRMM-3B43 have comparable performances. According to the spatial probability of detection (POD) and false alarm ratio (FAR) of the SPEI, more than 50% of all drought events cannot be accurately identified by QPE products in regions with sparse gauge distribution. In other regions, such as the southeastern USA, southeastern China, and South Africa, QPE products capture more than 75% of drought events. Temporally, all datasets (except for CMORPH-BLD) can detect all typical drought events, namely, in the southeastern US in 2007, western Europe in 2003, Kenya in 2006, and Central Asia in 2008. The study concludes that CHIRPS and TRMM-3B43 can be used as near-real-time drought monitoring techniques whereas PERSIANN-CDR might be more suitable for long-term historical drought analysis.

scholarly journals Global Climate Data Processing and Mapping of Degradation Mechanisms and Degradation Rates of PV Modules

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4749 ◽  
Author(s):  
Julián Ascencio-Vásquez ◽  
Ismail Kaaya ◽  
Kristijan Brecl ◽  
Karl-Anders Weiss ◽  
Marko Topič
Keyword(s):  
Degradation Rate ◽  
Global Climate ◽  
Global Scale ◽  
Degradation Mechanisms ◽  
Climate Data ◽  
Pv Systems ◽  
Degradation Rates ◽  
Pv Modules ◽  
Long Term Performance ◽  
Term Performance

Photovoltaic (PV) systems are the cheapest source of electricity in sunny locations and nearly all European countries. However, the fast deployment of PV systems around the world is bringing uncertainty to the PV community in terms of the reliability and long-term performance of PV modules under different climatic stresses, such as irradiation, temperature changes, and humidity. Methodologies and models to estimate the annual degradation rates of PV modules have been studied in the past, yet, an evaluation of the issue at global scale has not been addressed so far. Hereby, we process the ERA5 climate re-analysis dataset to extract and model the climatic stresses necessary for the calculation of degradation rates. These stresses are then applied to evaluate three degradation mechanisms (hydrolysis-degradation, thermomechanical-degradation, and photo- degradation) and the total degradation rate of PV modules due to the combination of temperature, humidity, and ultraviolet irradiation. Further on, spatial distribution of the degradation rates worldwide is computed and discussed proving direct correlation with the Köppen-Geiger-Photovoltaic climate zones, showing that the typical value considered for the degradation rate on PV design and manufacturer warranties (i.e., 0.5%/a) can vary ± 0.3%/a in the temperate zones of Europe and rise up to 1.5%/a globally. The mapping of degradation mechanisms and total degradation rates is provided for a monocrystalline silicon PV module. Additionally, we analyze the temporal evolution of degradation rates, where a global degradation rate is introduced and its dependence on global ambient temperature demonstrated. Finally, the categorization of degradation rates is made for Europe and worldwide to facilitate the understanding of the climatic stresses.

scholarly journals Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements

2021 ◽  
Author(s):  
Xinyan Li ◽  
Yuanjian Yang ◽  
Jiaqin Mi ◽  
Xueyan Bi ◽  
You Zhao ◽  
...  
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Large Scale ◽  
Predictive Ability ◽  
Summer Precipitation ◽  
Reanalysis Data ◽  
Probability Of Detection ◽  
Model Framework ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Abstract. Deriving large-scale and high-quality precipitation products from satellite remote sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using the China’s latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a Random Forest (RF) model framework for FY-4A QPE during daytime/nighttime is established by using FY-4A multi-band spectral information, cloud parameters, high-density precipitation observations, and physical quantities from reanalysis data. During daytime (nighttime), the probability of detection of the RF model for precipitation is 0.99 (0.99), while the correlation coefficient and root-mean-square error between the retrieved and observed precipitation are 0.77 (0.82) and 1.84 (2.32) mm/h, respectively, indicating that the RF model of FY-4A QPE has high precipitation retrieval accuracy. In particular, the RF model exhibits good spatiotemporal predictive ability for precipitation intensities within the range of 0.5–10 mm/h. For the retrieved accumulated precipitation, the precipitation intensity exhibits a greater impact on the predictive ability of the QPE algorithm than the precipitation duration. Due to the higher density of automatic stations in urban areas, the accuracy of FY-4A QPE over such areas is higher compared with rural areas. Both the accumulated precipitation and the distribution density of automatic stations are more important factors for the predictive ability of the RF model of FY-4A QPE. In general, our proposed FY-4A QPE algorithm has advantages for near-real-time monitoring of summer precipitation over East Asia.

scholarly journals Global meteorological drought – Part 1: Probabilistic monitoring

2014 ◽  
Vol 18 (7) ◽  
pp. 2657-2667 ◽  
Author(s):  
E. Dutra ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
G. Naumann ◽  
P. Barbosa ◽  
...  
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
Meteorological Drought ◽  
Global Scale ◽  
Added Value ◽  
Drought Monitoring ◽  
Data Set ◽  
Probabilistic Forecasts ◽  
Standardised Precipitation Index ◽  
Mean Square Errors

Abstract. Near-real-time drought monitoring can provide decision-makers with valuable information for use in several areas, such as water resources management, or international aid. One of the main constrains of assessing the current drought situation is associated with the lack of reliable sources of observed precipitation on a global scale available in near-real time. Furthermore, monitoring systems also need a long record of past observations to provide mean climatological conditions. To address these problems, a novel probabilistic drought monitoring methodology based on ECMWF probabilistic forecasts is presented, where probabilistic monthly means of precipitation were derived from short-range forecasts and merged with the long-term climatology of the Global Precipitation Climatology Centre (GPCC) data set. From the merged data set, the standardised precipitation index (SPI) was estimated. This methodology was compared with the GPCC first guess precipitation product as well as SPI calculations using the ECMWF ERA-Interim reanalysis and Tropical Rainfall Measuring Mission (TRMM) precipitation data sets. ECMWF probabilistic forecasts for near-real-time monitoring are similar to GPCC and TRMM in terms of correlation and root mean square errors, with the added value of including an estimate of the uncertainty given by the ensemble spread. The real-time availability of this product and its stability (i.e. that it does not directly depend on local rain gauges or single satellite products) are also beneficial in the light of an operational implementation.

scholarly journals Evaluation of PERSIANN-CDR Constructed Using GPCP V2.2 and V2.3 and A Comparison with TRMM 3B42 V7 and CPC Unified Gauge-Based Analysis in Global Scale

2019 ◽  
Vol 11 (23) ◽  
pp. 2755 ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Ata Akbari Asanjan ◽  
Mohammad Faridzad ◽  
Vesta Afzali Gorooh ◽  
Phu Nguyen ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Global Scale ◽  
Climate Data ◽  
Promising Alternative ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Precipitation Records ◽  
Global Precipitation ◽  
Trmm 3B42

Providing reliable long-term global precipitation records at high spatial and temporal resolutions is crucial for climatological studies. Satellite-based precipitation estimations are a promising alternative to rain gauges for providing homogeneous precipitation information. Most satellite-based precipitation products suffer from short-term data records, which make them unsuitable for various climatological and hydrological applications. However, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) provides more than 35 years of precipitation records at 0.25° × 0.25° spatial and daily temporal resolutions. The PERSIANN-CDR algorithm uses monthly Global Precipitation Climatology Project (GPCP) data, which has been recently updated to version 2.3, for reducing the biases in the output of the PERSIANN model. In this study, we constructed PERSIANN-CDR using the newest version of GPCP (V2.3). We compared the PERSIANN-CDR dataset that is constructed using GPCP V2.3 (from here on referred to as PERSIANN-CDR V2.3) with the PERSIANN-CDR constructed using GPCP V2.2 (from here on PERSIANN-CDR V2.2), at monthly and daily scales for the period from 2009 to 2013. First, we discuss the changes between PERSIANN-CDR V2.3 and V2.2 over the land and ocean. Second, we evaluate the improvements in PERSIANN-CDR V2.3 with respect to the Climate Prediction Center (CPC) unified gauge-based analysis, a gauged-based reference, and Tropical Rainfall Measuring Mission (TRMM 3B42 V7), a commonly used satellite reference, at monthly and daily scales. The results show noticeable differences between PERSIANN-CDR V2.3 and V2.2 over oceans between 40° and 60° latitude in both the northern and southern hemispheres. Monthly and daily scale comparisons of the two bias-adjusted versions of PERSIANN-CDR with the above-mentioned references emphasize that PERSIANN-CDR V2.3 has improved mostly over the global land area, especially over the CONUS and Australia. The updated PERSIANN-CDR V2.3 data has replaced V2.2 data for the 2009–2013 period on CHRS data portal and NOAA National Centers for Environmental Information (NCEI) Program.

scholarly journals Global meteorological drought – Part 1: Probabilistic monitoring

2014 ◽  
Vol 11 (1) ◽  
pp. 889-917 ◽  
Author(s):  
E. Dutra ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
G. Naumann ◽  
P. Barbosa ◽  
...  
Keyword(s):  
Real Time ◽  
Standardized Precipitation Index ◽  
Tropical Rainfall Measuring Mission ◽  
Meteorological Drought ◽  
Global Scale ◽  
Added Value ◽  
Drought Monitoring ◽  
Global Precipitation Climatology Centre ◽  
Probabilistic Forecasts ◽  
Mean Square Errors

Abstract. Near-real time drought monitoring can provide decision makers valuable information for use in several areas, such as water resources management, or international aid. One of the main constrains of assessing the current drought situation is associated with the lack of reliable sources of observed precipitation on a global scale available in near-real time. Furthermore, monitoring systems also need a long record of past observations to provide mean climatological conditions. To address these problems a novel probabilistic drought monitoring methodology based on ECMWF probabilistic forecasts is presented where probabilistic monthly means of precipitation were derived from short-range forecasts and merged with the long term climatology of the Global Precipitation Climatology Centre (GPCC) dataset. From the merged dataset, the Standardized Precipitation Index (SPI) was estimated. This methodology was compared with the GPCC first guess precipitation product and also SPI calculations using the ECMWF ERA-Interim reanalysis and Tropical Rainfall Measuring Mission (TRMM) precipitation datasets. ECMWF probabilistic forecasts for near-real time monitoring are similar to GPCC and TRMM in terms of correlation and root mean square errors, with the added value of including an estimate of the uncertainty given by the ensemble spread. The real time availability of this product and its stability, i.e. that it does not depend directly on local rain-gauges or single satellite products, are also beneficial in light of an operational implementation.

scholarly journals Assessment and Correction of the PERSIANN-CDR Product in the Yarlung Zangbo River Basin, China

2018 ◽  
Vol 10 (12) ◽  
pp. 2031 ◽  
Author(s):  
Jiangtao Liu ◽  
Zongxue Xu ◽  
Junrui Bai ◽  
Dingzhi Peng ◽  
Meifang Ren
Keyword(s):  
Correlation Coefficient ◽  
River Basin ◽  
Correction Method ◽  
Probability Of Detection ◽  
Climate Data ◽  
Precipitation Estimation ◽  
Original Product ◽  
Lhasa River ◽  
Correction Step ◽  
Unit Hydrographs

Satellite products can provide spatiotemporal data on precipitation in ungauged basins. It is essential and meaningful to assess and correct these products. In this study, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) product was evaluated and corrected using the successive correction method. A simple hydrological model was driven by the corrected PERSIANN-CDR data. The results showed that the accuracy of the original PERSIANN-CDR data was low on a daily scale, and the accuracy decreased gradually from the east to the west of the basin. With one correction step, the accuracy of the corrected PERSIANN-CDR data was significantly higher than that of the initial data. The correlation coefficient increased from 0.58 to 0.73, and the probability of detection (POD) value of the corrected product was 18.2% higher than the original product. The temporal-spatial resolution influenced the performance of the satellite product. As the resolution became coarser, the correlation coefficient between the corrected PERSIANN-CDR data and the gauged data gradually became lower. The Identification of unit Hydrographs and Component flows from Rainfall, Evapotranspiration, and Streamflow (IHACRES) model could be satisfactorily applied in the Lhasa River basin with corrected PERSIANN-CDR data. The successive correction method was an effective way to correct the bias of the PERSIANN-CDR product.

A methodological framework for assessing changes in future drought risk: evaluation over Australia

2021 ◽  
Author(s):  
Diogo Araujo ◽  
Francesco Marra ◽  
Cory Merow ◽  
Efthymios Nikolopoulos
Keyword(s):  
Climate Adaptation ◽  
Standardized Precipitation Index ◽  
Climate Extremes ◽  
Meteorological Drought ◽  
Global Scale ◽  
Mitigation Strategies ◽  
Drought Risk ◽  
Methodological Framework ◽  
Climate Data ◽  
Scientific Disciplines

<p>Accumulating evidence on the increase of intensity and frequency of climate extremes such as droughts, necessitates the development of effective climate adaptation procedures. To inform adaptation and mitigation strategies we need to develop improved methodologies for assessing future drought risk. The outputs of such methodologies must be usable by various stakeholders (e.g. water, energy and biodiversity conservation managers) and must be scalable (from regional to global) and methodologically robust. Severity-duration-frequency (SDF) curves serve as a concise way to quantitatively and qualitatively represent anticipated changes in drought risk and thus offer an optimum way to convey information on future drought risk across scientific disciplines and stakeholders. In this work we are presenting a methodological framework for assessing future drought risk that integrates state-of-the-art high-resolution (~10km) climate data from ERA5-Land reanalysis and downscaled CMIP6 projections with novel statistical procedures for robust estimation of SDF curves. Results are presented for Australia and are based on meteorological drought identification based on the widely established indicators of  Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Comparison between historic (1981-2019) and future (2020-2100) drought characteristics reveal that severity and duration tends to increase towards the end of the century. The spatial extent of severe and extreme droughts is also projected to increase, particularly in central and western Australia. The SDF analysis highlights a consistent increase in severity of extreme (i.e. 100yr) drought towards the end of century. While there is still significant uncertainty on the projected magnitude of increase, the multi-model analysis reveals that increasing trend of drought risk is consistent across models . The proposed framework can be applied at global scale and can be easily modified to incorporate additional drought indicators.</p>

Global-Scale Atmospheric Change with the Tibetan Plateau Uplift in a Coupled Climate Model (CESM)

2014 ◽  
Vol 962-965 ◽  
pp. 1392-1399
Author(s):  
Yan You Guo ◽  
Cheng Shan Wang ◽  
Jie Yang
Keyword(s):  
Tibetan Plateau ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Coupled Model ◽  
Vertical Motion ◽  
Global Scale ◽  
South Asian High ◽  
The Tibetan Plateau ◽  
Upper Troposphere

Large topography can affect the global climate change significantly. Many studies have revealed that the altitude of the Tibetan Plateau (TP) is related to the Asian regional climate. In order to find how the global-scale atmospheric circulation changes in response to the TP uplift in summer, a fully coupled model, Community Earth System Model (CESM), was used in this paper. Four experiments were run with the altitude of TP set to 25%, 50%, 75%, and 100% of the modern height, respectively (referred to as TP25, TP50, TP75, TP100 experiments). The results show that the uplift of the TP causes the change of the subtropical circulation over the northern hemisphere as well as the southern hemisphere. In the TP25 experiment, the South Asian High (SAH) at the 150mb is comparatively weak, and with the elevated surface heating, the vertical motion in the middle troposphere strengthens greatly. The ascending air over the TP leads to the forming and sustaining of the SAH, a dominate subsystem of the upper troposphere. The perturbation of the SAH propagates in the upper troposphere and stimulates stronger planetary waves on the globe and it also affects the circulation in the low level atmosphere. The subtropical highs over seal level strengthen over the Southern Ocean as well as the North Pacific and Atlantic Oceans. Furthermore, the westerlies around the Antarctica become stronger with the increasing altitude of the TP. This is meaningful that the stronger westerlies may be a forcing to accelerate the Cenozoic global cooling during the geological history.

New methodical approaches to mapping of geosystems (by the example of Baikalian Siberia’s geosystems)

2018 ◽  
Vol 937 (7) ◽  
pp. 23-34 ◽  
Author(s):  
I.N. Vladimirov
Keyword(s):  
Global Climate ◽  
Remote Sensing Data ◽  
Field Research ◽  
Scientific Basis ◽  
Nature Management ◽  
Climate Data ◽  
Structural Dynamic ◽  
Digital Elevation ◽  
North Asia ◽  
Elevation Model

The article considers a new approach to landscape mapping based on the synthesis of remote sensing data of high and medium spatial resolution, a digital elevation model, maps of various thematic contents, a set of global climate data, and materials of field research. The map of the Baikalian’s Siberia geosystems is based on the principles of the multistage regional-typological and structural-dynamic classification of geosystems proposed by Academician V.B. Sochava. The structure of the geosystems of the Baikalian Siberia is characterized by great complexity, both in the set of natural complexes and in the degree of their contrast. The regional classification range covers the geosystems inherent in different subcontinents of Asia and reflects their interpenetration, being a unique landscape-situational example of Siberian nature within North Asia. The map of the geosystems of the Baikalian Siberia reflects the main structural and dynamic diversity of geosystems in the region in the systems of their geographic and genetic spatial structures. These landscape cartographic studies fit into a single system of geographic forecasting and create a new fundamental scientific basis for developing recommendations for optimizing nature management in the Baikal region within the framework of implementing state environmental policy.

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

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