Estimating satellite-based precipitation at high latitudes is currently a major challenge in computing global rainfall products, as the current suite of satellite sensors cannot differentiate between cold or icy surface and frozen precipitation. With the advent of the Global Precipitation Mission (GPM), it is imperative that techniques and algorithms be developed to accurately estimate solid and liquid precipitation at high latitudes. To assess the current state of the art in high-latitude precipitation estimation, the Global Precipitation Climatology Project (GPCP) Version 2 (V2) monthly and One-Degree Daily (1DD) will be compared with high-quality Finnish Meteorological Institute (FMI) gauge observations.

The satellite-based GPCP V2 and 1DD have overcome the high-latitude instrument limitations through the use of Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) sounding data, with a transition to the Aqua Atmospheric Infrared Sounder (AIRS) in April 2005. The TOVS estimation technique infers precipitation from clouds using a regression relationship between coincident rain gauge measurements and TOVS-based parameters, including cloud-top pressure, fractional cloud cover, and cloud-layer relative humidity. As part of the GPCP processing, these TOVS precipitation estimates are then scaled to match the fractional coverage of the high-quality Special Sensor Microwave Imager (SSMI) estimates in the mid latitudes. The TOVS data are adjusted to the large-scale bias of the available rain gauge data at 70° and the large-scale bias of the SSMI estimates at mid latitudes.

High-quality ground-based precipitation estimates covering the high-latitude regions historically have not been easily accessible for research purposes. Recently, however, the FMI has made available a long record of research-quality high-density rain gauge observations. These gauges measure both liquid and solid (liquid-equivalent) precipitation. The temporal and spatial density of the FMI observations make them ideal candidates for comparison with and assessment of the GPCP satellite-based estimates. The goal is to quantify the nature of the differences at the high latitudes to further understand the errors associated with the satellite-based GPCP estimates, and to assess the future needs of the GPM. Large- and small-scale statistics such as bias, RMS, and correlation are examined. The results show that the bias difference between the GPCP estimates and FMI observations is dominated by the wind-loss correction applied to the gauge data that is incorporated into the GPCP. The magnitude of the wind-loss correction is smallest in the summer and largest in the winter. When the wind-loss correction is applied to the FMI observations, the long-term monthly correlation coefficient when compared to GPCP exceeds 0.9.