So, How Much of the Earth’s Surface Is Covered by Rain Gauges?

Abstract The measurement of global precipitation, both rainfall and snowfall, is critical to a wide range of users and applications. Rain gauges are indispensable in the measurement of precipitation, remaining the de facto standard for precipitation information across Earth’s surface for hydrometeorological purposes. However, their distribution across the globe is limited: over land their distribution and density is variable, while over oceans very few gauges exist and where measurements are made, they may not adequately reflect the rainfall amounts of the broader area. Critically, the number of gauges available, or appropriate for a particular study, varies greatly across the Earth owing to temporal sampling resolutions, periods of operation, data latency, and data access. Numbers of gauges range from a few thousand available in near–real time to about 100,000 for all “official” gauges, and to possibly hundreds of thousands if all possible gauges are included. Gauges routinely used in the generation of global precipitation products cover an equivalent area of between about 250 and 3,000 m2. For comparison, the center circle of a soccer pitch or tennis court is about 260 m2. Although each gauge should represent more than just the gauge orifice, autocorrelation distances of precipitation vary greatly with regime and the integration period. Assuming each Global Precipitation Climatology Centre (GPCC)–available gauge is independent and represents a surrounding area of 5-km radius, this represents only about 1% of Earth’s surface. The situation is further confounded for snowfall, which has a greater measurement uncertainty.

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Assessment of IMERG Precipitation Estimates over Europe

Remote Sensing ◽

10.3390/rs11212470 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2470 ◽

Cited By ~ 7

Author(s):

Navarro ◽

García-Ortega ◽

Merino ◽

Sánchez ◽

Kummerow ◽

...

Keyword(s):

Poor Performance ◽

Rain Gauge ◽

Spatial Gradient ◽

British Isles ◽

Mountainous Regions ◽

Rain Gauges ◽

Global Precipitation Climatology Centre ◽

Precipitation Estimates ◽

The Alps ◽

Global Precipitation

This paper evaluates Integrated Multi-Satellite Retrievals from GPM (IMERG-F) over Europe for the period 2014–2018 in order to evaluate application of the retrievals to hydrology. IMERG-F is compared with a large pan-European precipitation dataset built on rain gauge stations, i.e., the ENSEMBLES OBServation (E-OBS) gridded dataset. Although there is overall agreement in the spatial distribution of mean precipitation (R2 = 0.8), important discrepancies are revealed in mountainous regions, specifically the Alps, Pyrenees, west coast of the British Isles, Scandinavia, the Iberian and Italian peninsulas, and the Adriatic coastline. The results show that the strongest contributors to poor performance are pixels where IMERG-F has no gauges available for adjustment. If rain gauges are available, IMERG-F yields results similar to those of the surface observations, although the performance varies by region. However, even accounting for gauge adjustment, IMERG-F systematically underestimates precipitation in the Alps and Scandinavian mountains. Conversely, IMERG-F overestimates precipitation in the British Isles, Italian Peninsula, Adriatic coastline, and eastern European plains. Additionally, the research shows that gauge adjustment worsens the spatial gradient of precipitation because of the coarse resolution of Global Precipitation Climatology Centre data.

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The Intelligent Design of the Gear Reducer

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.522.823 ◽

2012 ◽

Vol 522 ◽

pp. 823-827

Author(s):

Jian Jiang Fang ◽

Wen Jun Qi

Keyword(s):

Intelligent Design ◽

Object Oriented ◽

Data Access ◽

Mechanical Transmission ◽

Design Efficiency ◽

Integrated Technology ◽

Gear Drive ◽

Object Oriented Technology ◽

Wide Range ◽

User Friendly

The gear drive is the wide range of applications and is particularly important as a form of mechanical transmission, but the design process requires large amounts of data access and computation. In the paper, computer integrated technology and object-oriented technology is used to research and develop the intelligent design of Straight gear reducer system with user-friendly interactive platform, easy to use, high design efficiency and reliable data.

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Rainfall spatio-temporal correlation and intermittency structure from micro-γ to meso-β scale in the Netherlands

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0311.1 ◽

2021 ◽

Author(s):

Thomas C. van Leth ◽

Hidde Leijnse ◽

Aart Overeem ◽

Remko Uijlenhoet

Keyword(s):

The Netherlands ◽

Spatial Scales ◽

Rainfall Variability ◽

Temporal Structure ◽

Temporal Correlation ◽

Exponential Model ◽

Rain Gauges ◽

Wide Range ◽

Spatio Temporal ◽

Two Parameter

AbstractWe investigate the spatio-temporal structure of rainfall at spatial scales from 7m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatio-temporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.

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A Dynamic Data Fragmentation and Distribution Strategy for Main-Memory Database Cluster

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.490-495.1231 ◽

2012 ◽

Vol 490-495 ◽

pp. 1231-1236 ◽

Cited By ~ 1

Author(s):

Tran Van Hung ◽

Chuan He Huang

Keyword(s):

Response Time ◽

Cluster Computing ◽

Data Access ◽

Fast Response ◽

Main Memory ◽

Relation Database ◽

Computing Platform ◽

Wide Range ◽

Cluster Environment ◽

Remote Sites

MMDB cluster system is a memory optimized relation database that implements on cluster computing platform, provides applications with extremely fast response time and very high throughput as required by many applications in a wide range of industries. Here, a new dynamic fragment allocation algorithm (DFAPR) in Partially Replicated allocation scenario is proposed. This algorithm reallocates data with respect to changing data access pattern for each fragment in which data is maintained in current site, migrated or created new replicas on remote sites depend on accessing frequency and average response time. At last, the simulation results show that the DFAPR is suitable for MMDB cluster because it provides a better response time and maximize the locality of processing so it could be developed parallel processing of MMDB in cluster environment.

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Updated gridded datasets version 2020 provided by the Global Precipitation Climatology Centre (GPCC)

10.5194/egusphere-egu21-14611 ◽

2021 ◽

Author(s):

Elke Rustemeier ◽

Udo Schneider ◽

Markus Ziese ◽

Peter Finger ◽

Andreas Becker

Keyword(s):

Quality Control ◽

Real Time ◽

Land Surface ◽

Quality Data ◽

Digital Object Identifier ◽

Full Data ◽

High Quality ◽

Precipitation Climatology ◽

Global Precipitation Climatology Centre ◽

Global Precipitation

Since its founding in 1989, the Global Precipitation Climatology Centre (GPCC) has been producing global precipitation analyses based on land surface in-situ measurements. In the now over 30 years the underlying database has been continuously expanded and includes a high station density and large temporal coverage. Due to the semi-automatic quality control routinely performed on the incoming station data, the GPCC database has a very high quality. Today, the GPCC holds data from 123,000 stations, about three quarters of them having long time series.The core of the analyses is formed by data from the global meteorological and hydrological services, which provided their records to the GPCC, as well as global and regional data collections.&#160; In addition, the GPCC receives SYNOP and CLIMAT reports via the WMO-GTS. These form a supplement for the high quality precipitation analyses and the basis for the near real-time evaluations.Quality control activities include cross-referencing stations from different sources, flagging of data errors, and correcting temporally or spatially offset data. This data then forms the basis for the following interpolation and product generation.In near real time, the 'First Guess Monthly', 'First Guess Daily', 'Monitoring Product', &#8216;Provisional Daily Precipitation Analysis&#8217; and the 'GPCC Drought Index' are generated. These are based on WMO-GTS data and monthly data generated by the CPC (NOAA). With a 2-3 year update cycle, the high quality data products are generated with intensive quality control and built on the entire GPCC data base. These non-real time products consist of the 'Full Data Monthly', 'Full Data Daily', 'Climatology', and 'HOMPRA-Europe' and are now available in the 2020 version. All gridded datasets presented in this paper are freely available in netcdf format on the GPCC website https://gpcc.dwd.de and referenced by a digital object identifier (DOI). The site also provides an overview of all datasets, as well as a detailed description and further references for each dataset.

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Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada

Hydrology and Earth System Sciences ◽

10.5194/hess-21-2163-2017 ◽

2017 ◽

Vol 21 (4) ◽

pp. 2163-2185 ◽

Cited By ~ 27

Author(s):

Jefferson S. Wong ◽

Saman Razavi ◽

Barrie R. Bonsal ◽

Howard S. Wheater ◽

Zilefac E. Asong

Keyword(s):

Large Scale ◽

Spatial And Temporal Variability ◽

The North ◽

Global Precipitation Climatology Centre ◽

Multiple Data ◽

Geographical Regions ◽

Central Canada ◽

Different Seasons ◽

Spatio Temporal ◽

Global Precipitation

Abstract. A number of global and regional gridded climate products based on multiple data sources are available that can potentially provide reliable estimates of precipitation for climate and hydrological studies. However, research into the consistency of these products for various regions has been limited and in many cases non-existent. This study inter-compares several gridded precipitation products over 15 terrestrial ecozones in Canada for different seasons. The spatial and temporal variability of the errors (relative to station observations) was quantified over the period of 1979 to 2012 at a 0.5° and daily spatio-temporal resolution. These datasets were assessed in their ability to represent the daily variability of precipitation amounts by four performance measures: percentage of bias, root mean square error, correlation coefficient, and standard deviation ratio. Results showed that most of the datasets were relatively skilful in central Canada. However, they tended to overestimate precipitation amounts in the west and underestimate in the north and east, with the underestimation being particularly dominant in northern Canada (above 60° N). The global product by WATCH Forcing Data ERA-Interim (WFDEI) augmented by Global Precipitation Climatology Centre (GPCC) data (WFDEI [GPCC]) performed best with respect to different metrics. The Canadian Precipitation Analysis (CaPA) product performed comparably with WFDEI [GPCC]; however, it only provides data starting in 2002. All the datasets performed best in summer, followed by autumn, spring, and winter in order of decreasing quality. Findings from this study can provide guidance to potential users regarding the performance of different precipitation products for a range of geographical regions and time periods.

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The Contribution of Rain Gauges in the Calibration of the IMERG Product: Results from the First Validation over Spain

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0116.1 ◽

2020 ◽

Vol 21 (2) ◽

pp. 161-182 ◽

Cited By ~ 4

Author(s):

Francisco J. Tapiador ◽

Andrés Navarro ◽

Eduardo García-Ortega ◽

Andrés Merino ◽

José Luis Sánchez ◽

...

Keyword(s):

Rain Gauge ◽

Surface Precipitation ◽

Rain Gauges ◽

Precipitation Measurement ◽

Precipitation Estimates ◽

Level 3 ◽

Hydrologic Impacts ◽

A Minor ◽

Global Precipitation Measurement ◽

Global Precipitation

AbstractAfter 5 years in orbit, the Global Precipitation Measurement (GPM) mission has produced enough quality-controlled data to allow the first validation of their precipitation estimates over Spain. High-quality gauge data from the meteorological network of the Spanish Meteorological Agency (AEMET) are used here to validate Integrated Multisatellite Retrievals for GPM (IMERG) level 3 estimates of surface precipitation. While aggregated values compare notably well, some differences are found in specific locations. The research investigates the sources of these discrepancies, which are found to be primarily related to the underestimation of orographic precipitation in the IMERG satellite products, as well as to the number of available gauges in the GPCC gauges used for calibrating IMERG. It is shown that IMERG provides suboptimal performance in poorly instrumented areas but that the estimate improves greatly when at least one rain gauge is available for the calibration process. A main, generally applicable conclusion from this research is that the IMERG satellite-derived estimates of precipitation are more useful (r2 > 0.80) for hydrology than interpolated fields of rain gauge measurements when at least one gauge is available for calibrating the satellite product. If no rain gauges were used, the results are still useful but with decreased mean performance (r2 ≈ 0.65). Such figures, however, are greatly improved if no coastal areas are included in the comparison. Removing them is a minor issue in terms of hydrologic impacts, as most rivers in Spain have their sources far from the coast.

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Validation of the First Years of GPM Operation over Cyprus

Remote Sensing ◽

10.3390/rs10101520 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1520 ◽

Cited By ~ 13

Author(s):

Adrianos Retalis ◽

Dimitris Katsanos ◽

Filippos Tymvios ◽

Silas Michaelides

Keyword(s):

High Resolution ◽

Prediction Models ◽

Weather Prediction ◽

Monthly Basis ◽

Rain Gauges ◽

Precipitation Measurement ◽

Precipitation Estimates ◽

First Results ◽

Global Precipitation Measurement ◽

Global Precipitation

Global Precipitation Measurement (GPM) high-resolution product is validated against rain gauges over the island of Cyprus for a three-year period, starting from April 2014. The precipitation estimates are available in both high temporal (half hourly) and spatial (10 km) resolution and combine data from all passive microwave instruments in the GPM constellation. The comparison performed is twofold: first the GPM data are compared with the precipitation measurements on a monthly basis and then the comparison focuses on extreme events, recorded throughout the first 3 years of GPM’s operation. The validation is based on ground data from a dense and reliable network of rain gauges, also available in high temporal (hourly) resolution. The first results show very good correlation regarding monthly values; however, the correspondence of GPM in extreme precipitation varies from “no correlation” to “high correlation”, depending on case. This study aims to verify the GPM rain estimates, since such a high-resolution dataset has numerous applications, including the assimilation in numerical weather prediction models and the study of flash floods with hydrological models.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-24-919-2020 ◽

2020 ◽

Vol 24 (2) ◽

pp. 919-943 ◽

Cited By ~ 9

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.

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Changes in TRMM Rainfall due to the Orbit Boost Estimated from Buoy Rain Gauge Data

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech2082.1 ◽

2007 ◽

Vol 24 (9) ◽

pp. 1598-1607 ◽

Cited By ~ 25

Author(s):

Jeremy D. DeMoss ◽

Kenneth P. Bowman

Keyword(s):

Sampling Error ◽

Tropical Rainfall Measuring Mission ◽

Rain Gauge ◽

Rain Gauge Data ◽

Rain Gauges ◽

Rate Dependent ◽

Wide Range ◽

The Mean ◽

Microwave Imager ◽

Trmm Microwave Imager

Abstract During the first three-and-a-half years of the Tropical Rainfall Measuring Mission (TRMM), the TRMM satellite operated at a nominal altitude of 350 km. To reduce drag, save maneuvering fuel, and prolong the mission lifetime, the orbit was boosted to 403 km in August 2001. The change in orbit altitude produced small changes in a wide range of observing parameters, including field-of-view size and viewing angles. Due to natural variability in rainfall and sampling error, it is not possible to evaluate possible changes in rainfall estimates from the satellite data alone. Changes in TRMM Microwave Imager (TMI) and the precipitation radar (PR) precipitation observations due to the orbit boost are estimated by comparing them with surface rain gauges on ocean buoys operated by the NOAA/Pacific Marine Environment Laboratory (PMEL). For each rain gauge, the bias between the satellite and the gauge for pre- and postboost time periods is computed. For the TMI, the satellite is biased ∼12% low relative to the gauges during the preboost period and ∼1% low during the postboost period. The mean change in bias relative to the gauges is approximately 0.4 mm day−1. The change in TMI bias is rain-rate-dependent, with larger changes in areas with higher mean precipitation rates. The PR is biased significantly low relative to the gauges during both boost periods, but the change in bias from the pre- to postboost period is not statistically significant.

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