scholarly journals 1 km Monthly Precipitation and Temperatures Dataset for China from 1952 to 2019 based on a Brand-New and High-Quality Baseline Climatology Surface

2021 ◽  
Author(s):  
Haibo Gong ◽  
Xueqiao Xiang ◽  
Huiyu Liu ◽  
Xiaojuan Xu ◽  
Fusheng Jiao ◽  
...  
Keyword(s):  
High Performance ◽  
Maximum Temperature ◽  
Monthly Precipitation ◽  
Climate Data ◽  
High Quality ◽  
Temperature Estimation ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
Better Than

Abstract. Long-term climate data and high-quality baseline climatology surface with high resolution are highly essential to multiple fields in climatological, ecological, hydrological, and environmental sciences. Here, we created a brand-new baseline climatology surface (ChinaClim_baseline) and developed a 1 km monthly precipitation and temperatures dataset in China during 1952–2019 (ChinaClim_timeseries). Thin plate spline (TPS) algorithm in each month with different model formulations by accounting for satellite-driven products, was used to generate ChinaClim_baseline and monthly climate anomaly surface. Meanwhile, climatologically aided interpolation (CAI) was used to superimpose monthly anomaly surface with ChinaClim_baseline to generate ChinaClim_timeseries. Our results showed that ChinaClim_baseline exhibited very high performance. For precipitation estimation, the value of all R2 was over 0.860, and the values of RMSEs and MAEs were 8.149 mm~21.959 mm and 2.787~14.125 mm, respectively. Temperature elements had an average R2 of 0.967~0.992, an average MAEs of 0.321~0.785 °C, and an average RMSEs between 0.485 and 1.233 °C for all months. ChinaClim_baseline performed much better than WorldClim2 and CHELSA and there were many spatial discrepancies captured among those surfaces, especially in summer months and the regions with low-density weather stations in temperate continental and high cold Tibetan Plateau. For ChinaClim_timeseries, precipitation had an average R2 of 0.699~0.923, an average RMSE between 7.449 mm and 56.756 mm, and an average of MAE of 4.263~40.271 mm for all months. Temperature elements had an average R2 of 0.936~0.985, an average RMSE between 0.807 °C and 1.766 °C, and an average MAE of 0.548~1.236 °C for all months. Compared with Peng's climate surface and CHELSAcruts, R2 increased by approximately 6 %, RMSE and MAE decreased by approximately 15 % for precipitation; R2 of temperatures had no obviously changes, but RMSE and MAE decreased by 8.37~34.02 %. The results showed that the interannual variations of ChinaClim_timeseries performed much better than other datasets, thanks to the help of ChinaClim_baseline and satellite-driven products. However, ChinaClim_baseline did not significantly improve the accuracy of precipitation estimation, but it greatly improved the accuracy of temperature estimation; the satellite-driven TRMM3B43 anomaly greatly improve the accuracy of precipitation estimation after 1998, while the LST anomaly did not effectively improve the accuracy of temperature estimation. ChinaClim_baseline can be used as an excellent baseline climatology surface for obtaining high-quality and long-term climate datasets from past to future. In the meantime, ChinaClim_timeseries of 1 km spatial resolution based on ChinaClim_baseline, is very suitable for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China. Here, ChinaClim_baseline is available at https://doi.org/10.5281/zenodo.4287824 (Gong, 2020a), ChinaClim_timeseries of precipitation is available at https://doi.org/10.5281/zenodo.4288388 (Gong, 2020b), ChinaClim_timeseries of maximum temperature is available at https://doi.org/10.5281/zenodo.4288390 (Gong, 2020c) and ChinaClim_timeseries of minimum temperature is available at https://doi.org/10.5281/zenodo.4288392 (Gong, 2020d).

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 The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

scholarly journals Performance Evaluation of Satellite-Based Rainfall Products over Nigeria

Climate ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 103
Author(s):  
Kingsley N. Ogbu ◽  
Nina Rholan Hounguè ◽  
Imoleayo E. Gbode ◽  
Bernhard Tischbein
Keyword(s):  
Agricultural Development ◽  
Rain Gauge ◽  
Local Economy ◽  
Climate Data ◽  
Climatic Zones ◽  
Rainfall Characteristics ◽  
Precipitation Estimation ◽  
Climate Data Record ◽  
Local Rainfall

Understanding the variability of rainfall is important for sustaining rain-dependent agriculture and driving the local economy of Nigeria. Paucity and inadequate rain gauge network across Nigeria has made satellite-based rainfall products (SRPs), which offer a complete spatial and consistent temporal coverage, a better alternative. However, the accuracy of these products must be ascertained before use in water resource developments and planning. In this study, the performances of Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT), were evaluated to investigate their ability to reproduce long term (1983–2013) observed rainfall characteristics derived from twenty-four (24) gauges in Nigeria. Results show that all products performed well in terms of capturing the observed annual cycle and spatial trends in all selected stations. Statistical evaluation of the SRPs performance show that CHIRPS agree more with observations in all climatic zones by reproducing the local rainfall characteristics. The performance of PERSIANN and TAMSAT, however, varies with season and across the climatic zones. Findings from this study highlight the benefits of using SRPs to augment or fill gaps in the distribution of local rainfall data, which is critical for water resources planning, agricultural development, and policy making.

A Comparison of Daily Temperature-Averaging Methods: Spatial Variability and Recent Change for the CONUS

2018 ◽  
Vol 31 (3) ◽  
pp. 979-996 ◽  
Author(s):  
Jase Bernhardt ◽  
Andrew M. Carleton ◽  
Chris LaMagna
Keyword(s):  
Spatial Variability ◽  
Daily Temperature ◽  
Maximum Temperature ◽  
Underlying Assumption ◽  
First Order ◽  
Daily Average ◽  
Hourly Temperature ◽  
Significant Difference ◽  
Weather Stations

Abstract Traditionally, the daily average air temperature at a weather station is computed by taking the mean of two values, the maximum temperature (Tmax) and the minimum temperature (Tmin), over a 24-h period. These values form the basis for numerous studies of long-term climatologies (e.g., 30-yr normals) and recent temperature trends and changes. However, many first-order weather stations—such as those at airports—also record hourly temperature data. Using an average of the 24 hourly temperature readings to compute daily average temperature has been shown to provide a more precise and representative estimate of a given day’s temperature. This study assesses the spatial variability of the differences in these two methods of daily temperature averaging [i.e., (Tmax + Tmin)/2; average of 24 hourly temperature values] for 215 first-order weather stations across the conterminous United States (CONUS) over the 30-yr period 1981–2010. A statistically significant difference is shown between the two methods, as well as consistent overestimation of temperature by the traditional method [(Tmax + Tmin)/2], particularly in southern and coastal portions of the CONUS. The explanation for the long-term difference between the two methods is the underlying assumption for the twice-daily method that the diurnal curve of temperature is symmetrical. Moreover, this paper demonstrates a spatially coherent pattern in the difference compared to the most recent part of the temperature record (2001–15). The spatial and temporal differences shown have implications for assessments of the physical factors influencing the diurnal temperature curve, as well as the exact magnitude of contemporary climate change.

scholarly journals Spatiotemporal Variations of Precipitation over Iran Using the High-Resolution and Nearly Four Decades Satellite-Based PERSIANN-CDR Dataset

2020 ◽  
Vol 12 (10) ◽  
pp. 1584 ◽  
Author(s):  
Hamidreza Mosaffa ◽  
Mojtaba Sadeghi ◽  
Negin Hayatbini ◽  
Vesta Afzali Gorooh ◽  
Ata Akbari Asanjan ◽  
...  
Keyword(s):  
Kendall Test ◽  
Rain Gauge ◽  
Annual Precipitation ◽  
Management Decision ◽  
Monthly Precipitation ◽  
Precipitation Trend ◽  
Climate Data ◽  
Suitable Alternative ◽  
Precipitation Estimation ◽  
Precipitation Trends

Spatiotemporal precipitation trend analysis provides valuable information for water management decision-making. Satellite-based precipitation products with high spatial and temporal resolution and long records, as opposed to temporally and spatially sparse rain gauge networks, are a suitable alternative to analyze precipitation trends over Iran. This study analyzes the trends in annual, seasonal, and monthly precipitation along with the contribution of each season and month in the annual precipitation over Iran for the 1983–2018 period. For the analyses, the Mann–Kendall test is applied to the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) estimates. The results of annual, seasonal, and monthly precipitation trends indicate that the significant decreases in the monthly precipitation trends in February over the western (March over the western and central-eastern) regions of Iran cause significant effects on winter (spring) and total annual precipitation. Moreover, the increases in the amounts of precipitation during November in the south and south-east regions lead to a remarkable increase in the amount of precipitation during the fall season. The analysis of the contribution of each season and month to annual precipitation in wet and dry years shows that dry years have critical impacts on decreasing monthly precipitation over a particular region. For instance, a remarkable decrease in precipitation amounts is detectable during dry years over the eastern, northeastern, and southwestern regions of Iran during March, April, and December, respectively. The results of this study show that PERSIANN-CDR is a valuable source of information in low-density gauge network areas, capturing spatiotemporal variation of precipitation.

scholarly journals Prediction Model suitable for Long-term High Turbidity Events in a Reservoir

2021 ◽  
Vol 21 (3) ◽  
pp. 203-213
Author(s):  
Junyoung Hong ◽  
Seokil Jeong ◽  
Byunghyun Kim
Keyword(s):  
Water Temperature ◽  
Input Data ◽  
Optimal Parameters ◽  
Climate Data ◽  
Flood Season ◽  
High Turbidity ◽  
Case Simulation ◽  
Temperature Climate ◽  
Better Than

The turbidity events caused by Typhoon “Kong-rey” in 2018 were selected as the measured turbidity data required to build the CE-QUAL-W2 model. The turbidity, water temperature, climate data, and discharge measured in the main dam were used as input data for numerical simulations. To ensure the reliability of the parameters, the turbidity events caused by Typhoon “Mitak” in 2019 were additionally selected, and optimal parameters for water temperature and other parameters were selected using case by case simulation. The analysis showed that the error rate and reliability were relatively good as the turnover phase approached through the turbidity rise and fall periods. The results of the verification of the maximum and average turbidity were better than those of the verification of the period-specific simulation. Thus, the models constructed in this study were determined to be suitable for the flood season.

Experimental warming and long-term vegetation dynamics in an alpine heathland

2013 ◽  
Vol 61 (1) ◽  
pp. 36 ◽  
Author(s):  
C.-H. Wahren ◽  
J. S. Camac ◽  
F. C. Jarrad ◽  
R. J. Williams ◽  
W. A. Papst ◽  
...  
Keyword(s):  
Linear Models ◽  
Sampling Period ◽  
Maximum Temperature ◽  
High Plains ◽  
High Mountain ◽  
Vegetation Changes ◽  
Experimental Warming ◽  
Alpine Vegetation ◽  
Climate Data

High mountain ecosystems are vulnerable to the effects of climate warming and Australia’s alpine vegetation has been identified as particularly vulnerable. Between 2004 and 2010, we monitored vegetation changes in a warming experiment within alpine open grassy-heathland on the Bogong High Plains, Victoria, Australia. The study was part of the International Tundra Experiment (ITEX Network) and used open-topped chambers (OTC) to raise ambient growing-season temperatures by ~1°C at two sites. We assessed the effects of experimental warming on vegetation composition, diversity and cover using ordination, linear models and hierarchical partitioning. Results were compared with vegetation changes at four long-term (non-ITEX) monitoring sites in similar vegetation sampled from 1979 to 2010. The warming experiment coincided with the driest 13-year period (1996–2009) since the late 1880s. At the ITEX sites, between 2004 and 2010, graminoid cover decreased by 25%, whereas forb and shrub cover increased by 9% and 20%, respectively. Mean canopy height increased from 7 cm to 10 cm and diversity increased as a result of changes in relative abundance, rather than an influx of new species. These vegetation changes were similar to those at the four non-ITEX sites for the same period and well within the range of changes observed over the 31-year sampling period. Changes at the non-ITEX sites were correlated with a decrease in annual precipitation, increase in mean minimum temperatures during spring and increase in mean maximum temperature during autumn. Vegetation changes induced by the warming experiment were small rather than transformational and broadly similar to changes at the long-term monitoring sites. This suggests that Australian alpine vegetation has a degree of resilience to climate change in the short to medium term (20–30 years). In the long term (>30 years), drought may be as important a determinant of environmental change in alpine vegetation as rising temperatures. Long-term vegetation and climate data are invaluable in interpreting results from short-term (≤10 years) experiments.

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