Abstract
This paper estimates rainfall totals at 17 Greenland meteorological stations, subjecting data from in situ precipitation gauge measurements to seven different precipitation phase schemes to separate rainfall and snowfall amounts. To correct the resulting snow/rain fractions for undercatch, we subsequently use a dynamic correction model (DCM) for automatic weather stations (AWS, Pluvio gauges) and a regression analysis correction method for staffed stations (Hellmann gauges). With observations ranging from 5% to 57% for cumulative totals, rainfall accounts for a considerable fraction of total annual precipitation over Greenland’s coastal regions, with the highest rain fraction in the south (Narsarsuaq). Monthly precipitation and rainfall totals are used to evaluate the regional climate model RACMO2.3. The model realistically captures monthly rainfall and total precipitation (R = 0.3–0.9), with generally higher correlations for rainfall for which the undercatch correction factors (1.02–1.40) are smaller than those for snowfall (1.27–2.80), and hence the observations are more robust. With a horizontal resolution of 5.5 km and simulation period from 1958 to the present, RACMO2.3 therefore is a useful tool to study spatial and temporal variability of rainfall in Greenland, although further statistical downscaling may be required to resolve the steep rainfall gradients.
| Original language | English |
|---|---|
| Pages (from-to) | 1171-1188 |
| Number of pages | 18 |
| Journal | Journal of Applied Meteorology and Climatology |
| Volume | 60 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2021 |
Bibliographical note
Funding Information:Acknowledgments. This publication was supported by PROTECT. This project has received funding from the European Union’s Horizon 2020 research and innovation program under a grant agreement (Grant 869304), PROTECT contribution number 20. Funding was also provided by the National Natural Science Foundation of China (Grant 41701059). The authors gratefully acknowledge data availability from the Danish Meteorological Institute (DMI), and the authors thank Brice Noël (Utrecht University) for RACMO2.3p2 data support.
Funding Information:
This publication was supported by PROTECT. This project has received funding from the European Union?s Horizon 2020 research and innovation program under a grant agreement (Grant 869304), PROTECT contribution number 20. Funding was also provided by the National Natural Science Foundation of China (Grant 41701059). The authors gratefully acknowledge data availability from the Danish Meteorological Institute (DMI), and the authors thank Brice No?l (Utrecht University) for RACMO2.3p2 data support.
Publisher Copyright:
© 2021 American Meteorological Society.
Funding
Acknowledgments. This publication was supported by PROTECT. This project has received funding from the European Union’s Horizon 2020 research and innovation program under a grant agreement (Grant 869304), PROTECT contribution number 20. Funding was also provided by the National Natural Science Foundation of China (Grant 41701059). The authors gratefully acknowledge data availability from the Danish Meteorological Institute (DMI), and the authors thank Brice Noël (Utrecht University) for RACMO2.3p2 data support. This publication was supported by PROTECT. This project has received funding from the European Union?s Horizon 2020 research and innovation program under a grant agreement (Grant 869304), PROTECT contribution number 20. Funding was also provided by the National Natural Science Foundation of China (Grant 41701059). The authors gratefully acknowledge data availability from the Danish Meteorological Institute (DMI), and the authors thank Brice No?l (Utrecht University) for RACMO2.3p2 data support.
Keywords
- Automatic weather stations
- Data quality control
- Gauges
- Precipitation
- Rainfall
- Regional models
