Evidence map›Paper›PMID 26646728›Full record

ArticleScientific data2015

The climate hazards infrared precipitation with stations--a new environmental record for monitoring extremes.

Chris Funk, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, James Rowland, Laura Harrison, Andrew Hoell and 1 more

Abstract readDataset
In one paragraph

Article in Scientific data, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 564 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
564citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

564 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  7. Global inequalities in weather forecasts.Nature communications · 2026
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504 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Chris FunkUS Geological Survey, Center for Earth Resources Observation and Science, 47914 252nd St., Sioux Falls, South Dakota 57198, USA.
Pete PetersonUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USA.
Martin LandsfeldUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USA.
Diego PedrerosUS Geological Survey, Center for Earth Resources Observation and Science, 47914 252nd St., Sioux Falls, South Dakota 57198, USA.
James VerdinUS Geological Survey, Center for Earth Resources Observation and Science, 47914 252nd St., Sioux Falls, South Dakota 57198, USA.
Shraddhanand ShuklaUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USA.
Gregory HusakUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USA.
James RowlandUS Geological Survey, Center for Earth Resources Observation and Science, 47914 252nd St., Sioux Falls, South Dakota 57198, USA.
Laura HarrisonUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USA.
Andrew HoellNational Oceanic and Atmospheric Administration Earth Systems Research Laboratory, Boulder, Colarodo 80305, USA.
Joel MichaelsenUC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to 'smart' interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.

Identifiers

PMID26646728
PMCPMC4672685

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.