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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. [ABSTRACT FROM AUTHOR]

Reanalysis datasets are frequently used in the study of atmospheric variability owing to their length of record and gridded global coverage. In the midlatitudes, much of the day-to-day atmospheric variability is associated with atmospheric fronts. These fronts are also responsible for the majority of precipitation in the midlatitudes, and are often associated with extreme weather, flooding, and wildfire activity. As such, it is important that identification of fronts and their associated rainfall remains as consistent as possible between studies. Nevertheless, it is often the case that only one reanalysis dataset and only one objective diagnostic for the detection of atmospheric fronts is used. By applying two different frontal identification methods across the shared time period of eight reanalysis datasets (1980–2001), it is found that the individual identification of fronts and frontal precipitation is significantly affected by both the choice of identification method and dataset. This is shown to subsequently impact the climatologies of both frontal frequency and frontal precipitation globally with significant regional differences as well. For example, for one diagnostic, the absolute multireanalysis range in the global mean frontal frequency and the proportion of precipitation attributed to atmospheric fronts are 12% and 69%, respectively. A percentage reduction of 77% and 81%, respectively, in these absolute multireanalysis ranges occurs, however, upon regridding all datasets to the same coarser grid. Therefore, these findings have important implications for any study on precipitation variability and not just those that consider atmospheric fronts. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) at mesoscales is important for severe weather forecasts, yet the techniques of data assimilation at this scale remain a challenge. This study introduces dynamical constraints in the Gridpoint Statistical Interpolation (GSI) three-dimensional ensemble variational (3D-EnVar) data assimilation algorithm to enable the use of high-resolution surface observations of precipitation to improve atmospheric analysis at mesoscales. The constraints use the conservations of mass and moisture. Mass constraint suppresses the unphysical high-frequency oscillation, while moisture conservation constrains the atmospheric states to conform with the observed high-resolution precipitation. We show that the constrained data assimilation (CDA) algorithm significantly reduced the spurious residuals of the mass and moisture budgets compared to the original data assimilation (ODA). A case study is presented for a squall line over the Southern Great Plains on 20 May 2011 during Midlatitude Continental Convective Clouds Experiment (MC3E) of the Atmospheric Radiation Measurement (ARM) program by using ODA or CDA analysis as initial condition of forecasts. The state variables, and the location and intensity of the squall line are better simulated in the CDA experiment. Results show how surface observation of precipitation can be used to improve atmospheric analysis through data assimilation by using the dynamical constraints of mass and moisture conservations. [ABSTRACT FROM AUTHOR]