scholarly journals Interpolation of Global Monthly Rain Gauge Observations for Climate Change Analysis

2015 ◽  
Vol 54 (7) ◽  
pp. 1449-1464 ◽  
Author(s):  
Jürgen Grieser
Keyword(s):  
Rain Gauge ◽  
Monthly Precipitation ◽  
Change Analysis ◽  
Global Precipitation Climatology Centre ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Surface Climate ◽  
Local Station ◽  
Global Precipitation

AbstractLong-term global gridded datasets of observed precipitation are essential for the analysis of the global water and energy cycle, its variability, and possible changes. Several institutions provide those datasets. In 2005 the Global Precipitation Climatology Centre (GPCC) published the so-called Variability Analysis of Surface Climate Observations (VASClimO) dataset. This dataset is especially designed for the investigation of temporal change and variability. To date, however, the GPCC has not published how this dataset has been produced. This paper aims to fill this gap. It provides detailed information on how stations are selected and how data are quality controlled and interpolated. The dataset is based only on station records covering at least 90% of the period 1951–2000. The time series of 9343 stations were used. However, these stations are distributed very inhomogeneously around the globe; 4094 of these stations are within Germany and France. The VASClimO dataset is interpolated from relative deviations of observed monthly precipitation, leading to considerably lower interpolation errors than direct interpolation or the interpolation of absolute deviations. The retransformation from interpolated relative deviations to precipitation is done with local long-term averages of precipitation interpolated from data of the Food and Agriculture Organization of the United Nations. The VASClimO dataset has been interpolated with a method that is based on local station correlations (LSC) that is introduced here. It is compared with ordinary kriging and three versions of Shepard’s method. LSC outperforms these methods, especially with respect to the spatial maxima of interpolation errors.

Comparison of Gridded Multisatellite Rainfall Estimates with Gridded Gauge Rainfall over West Africa

2008 ◽  
Vol 47 (1) ◽  
pp. 185-205 ◽  
Author(s):  
Benjamin L. Lamptey
Keyword(s):  
Global Precipitation Climatology Project ◽  
Rain Gauge ◽  
Guinea Coast ◽  
Rain Gauge Data ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Surface Climate ◽  
Gauge Data ◽  
Global Precipitation ◽  
Rainfall Climatology

Abstract Two monthly gridded precipitation datasets of the Global Precipitation Climatology Project (GPCP; the multisatellite product) and the Global Precipitation Climatology Centre (GPCC) Variability Analysis of Surface Climate Observations (VASClimO; rain gauge data) are compared for a 22-yr period, from January 1979 to December 2000, over land areas (i.e., latitudes 4°–20°N and longitudes 18°W–15°E). The two datasets are consistent with respect to the spatial distribution of the annual and seasonal rainfall climatology over the domain and along latitudinal bands. However, the satellite generally overestimates rainfall. The inability of the GPCC data to capture the bimodal rainfall pattern along the Guinea coast (i.e., south of latitude 8°N) is an artifact of the interpolation of the rain gauge data. For interannual variability, the gridded multisatellite and gridded gauge datasets agree on the sign of the anomaly 15 out of the 22 yr (68% of the time) for region 1 (between longitude 5° and 18°W and north of latitude 8°N) and 18 out of the 22 yr (82% of the time) for region 2 (between longitude 5°W and 15°E and north of latitude 8°N). The datasets agreed on the sign of the anomaly 14 out of the 22 yr (64% of the time) over the Guinea Coast. The magnitudes of the anomaly are very different in all years. Most of the years during which the two datasets did not agree on the sign of the anomaly were years with El Niño events. The ratio of the seasonal root-mean-square differences to the seasonal mean rainfall range between 0.24 and 2.60. The Kendall’s tau statistic indicated statistically significant trends in both datasets, separately.

scholarly journals Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016

2020 ◽  
Vol 24 (2) ◽  
pp. 919-943 ◽  
Author(s):  
Steefan Contractor ◽  
Markus G. Donat ◽  
Lisa V. Alexander ◽  
Markus Ziese ◽  
Anja Meyer-Christoffer ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
In Situ Data ◽  
Global Precipitation Climatology Centre ◽  
Historical Climatology ◽  
Global Land ◽  
Global Historical Climatology Network ◽  
Global Precipitation ◽  
Rainfall Estimates

Abstract. We present a new global land-based daily precipitation dataset from 1950 using an interpolated network of in situ data called Rainfall Estimates on a Gridded Network – REGEN. We merged multiple archives of in situ data including two of the largest archives, the Global Historical Climatology Network – Daily (GHCN-Daily) hosted by National Centres of Environmental Information (NCEI), USA, and one hosted by the Global Precipitation Climatology Centre (GPCC) operated by Deutscher Wetterdienst (DWD). This resulted in an unprecedented station density compared to existing datasets. The station time series were quality-controlled using strict criteria and flagged values were removed. Remaining values were interpolated to create area-average estimates of daily precipitation for global land areas on a 1∘ × 1∘ latitude–longitude resolution. Besides the daily precipitation amounts, fields of standard deviation, kriging error and number of stations are also provided. We also provide a quality mask based on these uncertainty measures. For those interested in a dataset with lower station network variability we also provide a related dataset based on a network of long-term stations which interpolates stations with a record length of at least 40 years. The REGEN datasets are expected to contribute to the advancement of hydrological science and practice by facilitating studies aiming to understand changes and variability in several aspects of daily precipitation distributions, extremes and measures of hydrological intensity. Here we document the development of the dataset and guidelines for best practices for users with regards to the two datasets.

Satellite soil moisture improves rainfall just where needed

2020 ◽  
Author(s):  
Luca Brocca ◽  
Stefania Camici ◽  
Christian Massari ◽  
Luca Ciabatta ◽  
Paolo Filippucci ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Discharge ◽  
Large Scale ◽  
River Flow ◽  
Global Scale ◽  
Rain Gauge ◽  
Rainfall Runoff ◽  
Global Precipitation Climatology Centre ◽  
Satellite Rainfall ◽  
Global Precipitation

<p>Soil moisture is a fundamental variable in the water and energy cycle and its knowledge in many applications is crucial. In the last decade, some authors have proposed the use of satellite soil moisture for estimating and improving rainfall, doing hydrology backward. From this research idea, several studies have been published and currently preoperational satellite rainfall products exploiting satellite soil moisture products have been made available.</p><p>The assessment of such products on a global scale has revealed an important result, i.e., the soil moisture based products perform better than state of the art products exactly over regions in which the data are needed: Africa and South America. However, over these areas the assessment against rain gauge observations is problematic and independent approaches are needed to assess the quality of such products and their potential benefit in hydrological applications. On this basis, the use of the satellite rainfall products as input into rainfall-runoff models, and their indirect assessment through river discharge observations is an alternative and valuable approach for evaluating their quality.</p><p>For this study, a newly developed large scale dataset of river discharge observations over 500+ basins throughout Africa has been exploited. Based on such unique dataset, a large scale assessment of multiple near real time satellite rainfall products has been performed: (1) the Early Run version of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement), IMERG Early Run, (2) SM2RAIN-ASCAT (https://doi.org/10.5281/zenodo.3405563), and (3) GPM+SM2RAIN (http://doi.org/10.5281/zenodo.3345323). Additionally, gauge-based and reanalysis rainfall products have been considered, i.e., (4) the Global Precipitation Climatology Centre (GPCC), and (5) the latest European Centre for Medium-Range Weather Forecasts reanalysis, ERA5. As rainfall-runoff model, the semi-distributed MISDc (Modello Idrologico Semi-Distribuito in continuo) model has been employed in the period 2007-2018 at daily temporal scale.</p><p>First results over a part of the dataset reveal the great value of satellite soil moisture products in improving satellite rainfall estimates for river flow prediction in Africa. Such results highlight the need to exploit such products for operational systems in Africa addressed to the mitigation of the flood risk and water resources management.</p>

scholarly journals GPCC Drought Index – a new, combined, and gridded global drought index

2014 ◽  
Vol 7 (1) ◽  
pp. 243-270
Author(s):  
M. Ziese ◽  
U. Schneider ◽  
A. Meyer-Christoffer ◽  
K. Schamm ◽  
J. Vido ◽  
...  
Keyword(s):  
Drought Index ◽  
Standardized Precipitation Index ◽  
Regular Grid ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Precipitation Anomalies ◽  
The Standardized Precipitation Index ◽  
Global Precipitation ◽  
Climate Prediction Center

Abstract. The Global Precipitation Climatology Centre Drought Index (GPCC-DI) provides estimations of precipitation anomalies with respect to long term statistics. It is a combination of the Standardized Precipitation Index with adaptations from Deutscher Wetterdienst (SPI-DWD) and the Standardized Precipitation Evapotranspiration Index (SPEI). Precipitation data were taken from the Global Precipitation Climatology Centre (GPCC) and temperature data from NOAA's Climate Prediction Center (CPC). The GPCC-DI is available with several averaging periods of 1, 3, 6, 9, 12, 24 and 48 months for different applications. Since spring 2013, the GPCC-DI is calculated operationally and available back to January 2013. Typically it is released at the 10th day of the following month, depending on the availability of the input data. It is calculated on a~regular grid with 1° spatial resolution. All averaging periods are integrated into one netCDF-file for each month. This dataset can be referenced by the DOI:10.5676/DWD_GPCC/DI_M_100 and is available free of charge from the GPCC website ftp://ftp.dwd.de/pub/data/gpcc/html/gpcc_di_doi_download.html.

An Overview of Illegal Logging Situation in Peninsular Malaysia

2013 ◽  
Vol 1 (2) ◽  
pp. 24-30
Author(s):  
Iqtie Qamar Laila Mohd Gani ◽  
Razak Wahab ◽  
Mohd Sukhairi Mat Rasat
Keyword(s):  
Negative Relationship ◽  
Peninsular Malaysia ◽  
Small Scale ◽  
Illegal Logging ◽  
Tropical Timber ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
The Government ◽  
The Relationship

The trends of illegal logging and current situation of illegal logging in Peninsular Malaysia were studies. Data and information from year 2001 to 2010 on volume of log productions (m3) and volume of illegal log productions were collected from the government and private sectors such as the Forestry Department Peninsular Malaysia (FDPM), International Tropical Timber Organization (ITTO) and Food and Agriculture Organization (FAO). The data obtained were statistically analyzed using the correlation analysis to determine the direction and the strength of the relationship between log productions and illegal log productions. The results showed that the trends of illegal logging are on the increased. Eighteen percents (18%) of the logs cut annually are obtained from illegal operation. The log productions and illegal log productions resulted have a weak negative relationship as r = -0.271, p = 0.603 and do not significantly related. The illegal log productions are inversely related with the log productions. It can be concluded that the log productions in Peninsular Malaysia occurred in a small scale and the situation is under control. Proper long-term planning needs to be generated and implemented to prevent the problem from becoming worse.

scholarly journals Evaluation of Satellite-Based and Reanalysis Precipitation Data in the Tropical Pacific

2013 ◽  
Vol 52 (3) ◽  
pp. 634-644 ◽  
Author(s):  
Uwe Pfeifroth ◽  
Richard Mueller ◽  
Bodo Ahrens
Keyword(s):  
Atmospheric Model ◽  
Rain Gauge ◽  
Tropical Pacific ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Model Based ◽  
Station Data ◽  
Global Precipitation ◽  
Temporal Coverage ◽  
The Tropical Pacific

AbstractGlobal precipitation monitoring is essential for understanding the earth’s water and energy cycle. Therefore, usage of satellite-based precipitation data is necessary where in situ data are rare. In addition, atmospheric-model-based reanalysis data feature global data coverage and offer a full catalog of atmospheric variables including precipitation. In this study, two model-based reanalysis products, the interim reanalysis by the European Centre for Medium-Range Weather Forecasts (ERA-Interim) and NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA), as well as two satellite-based datasets obtained by the Global Precipitation Climatology Centre (GPCP) and Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) are evaluated. The evaluation is based on monthly precipitation in the tropical Pacific Ocean during the time period 1989–2005. Rain-gauge atoll station data provided by the Pacific Rainfall Database (PACRAIN) are used as ground-based reference. It is shown that the analyzed precipitation datasets offer temporal correlations ranging from 0.7 to 0.8 for absolute amounts and from 0.6 to 0.75 for monthly anomalies. Average monthly deviations are in the range of 20%–30%. GPCP offers the highest correlation and lowest monthly deviations with reference to PACRAIN station data. The HOAPS precipitation data perform in the range of the reanalysis precipitation datasets. In high native spatial resolution, HOAPS reveals deficiencies owing to its relatively sparse temporal coverage. This result emphasizes that temporal coverage is critical for controlling the performance of precipitation monitoring. Both reanalysis products show similar systematic behaviors in overestimating small and medium precipitation amounts and underestimating high amounts.

scholarly journals Rainfall Estimates on a Gridded Network (REGEN) – A global land-based gridded dataset of daily precipitation from 1950–2013

2019 ◽  
Author(s):  
Steefan Contractor ◽  
Markus G. Donat ◽  
Lisa V. Alexander ◽  
Markus Ziese ◽  
Anja Meyer-Christoffer ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
In Situ Data ◽  
Global Precipitation Climatology Centre ◽  
Historical Climatology ◽  
Global Land ◽  
Global Historical Climatology Network ◽  
Global Precipitation ◽  
Rainfall Estimates

Abstract. We present a new global land-based daily precipitation dataset from 1950 using an interpolated network of in situ data called Rainfall Estimates on a GriddEd Network – REGEN. We merged multiple archives of in situ data including two of the largest archives, the Global Historical Climatology Network – Daily (GHCN-Daily) hosted by National Centres of Environmental Information (NCEI), USA and one hosted by the Global Precipitation Climatology Centre (GPCC) operated by Deutscher Wetterdienst (DWD). This resulted in an unprecedented station density compared to existing datasets. The station timeseries were quality controlled using strict criteria and flagged values were removed. Remaining values were interpolated to create area average estimates of daily precipitation for global land areas on a 1° × 1° latitude–longitude resolution. Besides the daily precipitation amounts, fields of standard deviation, Kriging error and number of stations are also provided. We also provide a quality mask based on these uncertainty measures. For those interested in a dataset with lower station network variability we also provide a related dataset based on a network of long-term stations which interpolates stations with a record length of at least 40 years. The REGEN datasets are expected to contribute to the advancement of hydrological science and practice by facilitating studies aiming to understand changes and variability in several aspects of daily precipitation distributions, extremes, and measures of hydrological intensity. Here we document the development of the dataset and guidelines for best practices for users with regards to the two datasets.

scholarly journals Scrutinizing Pork Price Volatility in the European Union over the Last Decade

Animals ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 100
Author(s):  
Katarzyna Utnik-Banaś ◽  
Tomasz Schwarz ◽  
Elzbieta Jadwiga Szymanska ◽  
Pawel Mieczyslaw Bartlewski ◽  
Łukasz Satoła
Keyword(s):  
European Union ◽  
Time Series ◽  
Price Volatility ◽  
Integrated System ◽  
The European Union ◽  
Pork Production ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Cyclical Component

The aim of this study was to analyze the factors that can influence pork prices, particularly the effects of various types of fluctuations on the volatility of pork prices in the European Union as a whole market and individual EU countries. The research material consisted of monthly time series of pork prices collected from 2009 to 2020. These data originated from the Integrated System of Agricultural Information coordinated by the Polish Ministry of Agriculture. Information on global pork production volumes is from the Food and Agriculture Organization Statistics (FAOSTAT) database. Time series of prices were described by the multiplicative model, and seasonal breakdown was performed using the Census X-11 method. The separation of the cyclical component of the trend was performed using the Hodrick–Prescott filter. In 2019, pork production in the European Union totaled 23,954 thousand tonnes, which accounted for 21.8% of global pork production. The largest producers were Germany, Spain, and France, supplying more than half of the pork to the entire European Union market. Pork prices in the EU, averaged over the 2009–2020 period were Euro (EUR) 154.63/100 kg. The highest prices for pork were recorded in Malta, Cyprus, Bulgaria, and Greece, whereas the lowest prices in Belgium, the Netherlands, Denmark, and France. The breakdown of the time series for pork prices confirmed that, in the period from 2009 to 2020, pork prices exhibited considerable fluctuations of both a long-term and medium-term nature as well as short-term seasonal and irregular fluctuations. Prices were higher than average in summer (with a peak in June–August) and lower in winter (January–March). Overall, the proportions of different types of changes in pork prices were as follows: random changes—7.9%, seasonal changes—36.6%, and cyclical changes—55.5%.

Sahelian countries face acute hunger crisis

2018 ◽  
Keyword(s):  
Climate Change ◽  
Rural Economy ◽  
Acute Malnutrition ◽  
Content Type ◽  
Crisis Prevention ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Food Shortages ◽  
The Cost

Subject Food shortages and insecurity in the Sahel. Significance The UN Food and Agriculture Organization (FAO), the UN Children's Fund (UNICEF) and the World Food Programme (WFP) warned in May that 1.6 million children are in danger of acute malnutrition and 5.0 million people are in need of food assistance in the Sahel and parts of West Africa. Drought and conflict have left 7.1 million people in the Sahel in need of food aid, the Food Crisis Prevention Network (RPCA) warned in April. Pastoral societies are severely affected. The range of food shortages and socio-economic crises in the Sahel reflects the region’s vulnerability to annual drought and long-term climate change, as well as challenges posed by insecurity. Impacts France will emphasise counter-terrorism even though the cost of fighting terrorism undermines the Sahel’s capacity to avert food crises. The region will remain vulnerable to annual droughts. Long-term climate change will threaten the sustainability of the rural economy upon which the majority of the population still depend.

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