Gregor Skok

Object-Based Analysis of Satellite-Derived Precipitation Systems over the Low- and Midlatitude Pacific Ocean

Abstract The Method for Object-based Diagnostic Evaluation (MODE) developed by Davis et al. is implemented and extended to characterize the temporal behavior of objects and to perform a diagnostic analysis on the spatial distribution and properties of precipitation systems over the equatorial Pacific Ocean. The analysis is performed on two satellite-derived datasets [Tropical Rainfall Measuring Mission (TRMM) 3B42 and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)]. A sensitivity analysis showed that temporal convolution produces an unwanted “spillover” effect and that a large spatial convolution radius produces too much smoothing, which results in unrealistically large objects. The analysis showed that the largest and most long-lived precipitation systems in the tropical Pacific are typically located in the western part. A good ability to track precipitation systems in the tropical Pacific was demonstrated: movement of precipitation systems in the IT...

Analysis of Tropical Cyclone Precipitation Using an Object-Based Algorithm

AbstractA recently developed object identification algorithm is applied to multisensor precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM 3B42) to detect and quantify the contribution of tropical cyclone precipitation (TCP) to total precipitation between 1998 and 2008. The study period includes 1144 storms. Estimates of TCP derived here are similar in pattern and seasonal variation to earlier estimates but are somewhat higher in magnitude. Annual-mean TCP fractions of over 20% are diagnosed over large swaths of tropical ocean, with seasonal means in some regions of more than 50%. Interannual variability of TCP is examined, and a small but significant downward trend in global TCP from 1998 to 2008 is found, consistent with results from independent studies examining accumulated cyclone energy (ACE). Relationships between annual-mean ACE and TCP in each major tropical cyclone basin are examined. High correlations are found in almost every basin, although different linear relationships...

Object-Based Analysis and Verification of WRF Model Precipitation in the Low- and Midlatitude Pacific Ocean

Abstract An extended version of the Method for Object-based Diagnostic Evaluation (MODE) was used to perform a verification of precipitation provided by the Weather Research and Forecasting (WRF) model Tropical Channel Simulation (performed by NCAR). Model 3-hourly precipitation accumulations were compared to the Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite-derived precipitation in the low- and midlatitude Pacific Ocean during 1998–2000. Overall, the spatial distribution of annual mean precipitation (i.e., the clear shape of the ITCZ with two maxima—one located in the east and one in the west) was reproduced well by the WRF model; however, there was considerably more precipitation in the WRF simulation than in the TRMM dataset. Object-based analysis identified more precipitation objects with life spans less than 30 h and fewer objects with life spans longer than 60 h in the TRMM dataset compared to the WRF simulation. Objects with the longest life span (>90 h) tended to occur in similar regio...

Considerations for interpolating rain gauge precipitation onto a regular grid

The aim of this study is to used heavy precipitation data from Slovenia to find the highest resolution from the Numerical Weather Prediction model where the model cell averaged interpolated fields of 24hour rain gauge precipitation which was independent of the interpolation method. 20 out of 104 observed heavy precipitation cases from the period of 1995-2002 in Slovenia were selected, in which 10 were convective and 10 stratiform. Three different interpolation methods were used: Universal Kriging, Inverse Distance Weighted and Radial Basis Functions. Each method was aggregated and direct interpolation was performed. The cross-validation results showed that the interpolation errors were as large as 50 % of the observed precipitation maximum (in our case from 50 to 110 mm). This indicates that an interpolation into high resolution or into a point is not reliable. Also, the interpolation cross-validation errors, the Root Mean Squared Error and Mean Absolute Error, were larger for stratiform precipitation than for convective precipitation. The reason for these errors seems to be that for the convective cases there was a smaller area with heavy precipitation and a large area with weak or no precipitation. The smaller area with spatially very variable precipitation contributed significantly to errors while the larger did not. Conversely, this was not the case for stratiform precipitation cases where precipitation and corresponding errors occurred all over the domain. Model grids were interpolated with three different resolutions of 4, 8 and 16 km. The condition for a method-independent interpolation entitled that every model grid cell had to have at least one to two rain gauges inside and possibly more in the very near vicinity. The highest possible model resolution should therefore be close or lower as the double of average distance to the closest neighbor in the rain gauge network, which in our case occurred at 16 km. Also, a certain procedure for interpolation was followed: first, the interpolation into high resolution and secondly, aggregation into the final resolution. A simple interpolation into a point in the middle of the model grid cell did not produce interpolation independent results even at a lower resolution. When using the right procedure at 16 km, the maximum differences between interpolation methods were usually less than 15 % of the observed precipitation maximum.

Relief shapes and precipitation on the south side of the Alps. Part II: Heavy-rain cases during MAP and sensitivity to topography modifications

There are two distinct climatological precipitation maxima on the south side of the Alps and an area of reduced precipitation between them. We tested the impact of meso-beta details of topography on precipitation by simulating 15 cases of heavy precipitation during the Mesoscale Alpine Programme (MAP) special observing period with the MM5 model. Data show that the selected 15 cases are climatologically representative since the precipitation amount for these 15 days shows two climatological precipitation maxima and a drier region between them. We used different idealized topographies of the Alps, as well as the real topography and modifications of the latter at meso-beta scale. The simulations with different idealized topographies show clear influence of supra-regional (scale of some 100 km) convergence upon correct location of the southern Alpine maxima and that the concave terrain shapes at this scale determine the location of the precipitation maxima. The simulations with modified real topography show that (i) in the case of removal of the southwards bulging Trentino Mts. the precipitation maximum concentrates at this modified region; (ii) with removal of the Dinaric Alps the precipitation on the eastern part of the Alps is significantly reduced.

Analytical and practical examples of estimating the average nearest-neighbor distance in a rain gauge network

This paper deals with estimating the average n-th neighbor distance in a rain gauge network using analytical expressions. Research was carried out to find the relationship between the total number of rain gauges, the area size, and the shape of a domain on one side and the average distance to the n-th neighbor on the other side. Authors such as HERTZ (1909) and CHANDRASEKHAR (1943) have shown that when a random distribution of rain gauges and an infinite domain are assumed, a simple analytical formula can be obtained. For a circular domain, one gets a first neighbor formula, given by an integral which has to be integrated numerically. For neighbors located further away and other domain shapes, meaning not infinite and not circular, the best way to estimate the average n-th neighbor distance seems to be a computer program which uses a random number generator to distribute the rain gauges in a domain. After the process of random distribution is complete, then the distance to the n-th neighbor is found for each rain gauge. These distances are then averaged. We performed tests using seven domain shapes and the results show that the average distance to the nearest neighbor is always larger in a limited domain rather than in an infinite domain with the same rain gauge density. The difference between the average distance in an infinite and in a particular domain depends on the shape of the domain and the total number of rain gauges within it. The length of a boundary does not inherently influence the magnitude of the difference. However, the difference is small if the total number of rain gauges is large and a simple infinite domain equation can usually be used. For any tested domain the difference is less than 10 % if the number of rain gauges is larger than 100. The assumption of randomly distributed rain gauges limits the usefulness of the method. There are cases, such as smaller countries or country sub-regions, where the rain gauges might be more or less evenly scattered in the domain. In these cases the method is still useful and the distance to the n-th neighbor could be estimated using a simple analytical expression.

Verification of Gridded Wind Forecasts in Complex Alpine Terrain: A New Wind Verification Methodology Based on the Neighborhood Approach

AbstractA novel wind verification methodology is presented and analyzed for six surface wind cases in the greater Alpine region as well as an idealized setup. The methodology is based on the idea of the fractions skill score, a neighborhood-based spatial verification metric frequently used for verifying precipitation. The new score avoids the problems of traditional nonspatial verification metrics (the “double penalty” problem and the failure to distinguish between a “near miss” and much poorer forecasts) and can distinguish forecasts even when the spatial displacement of wind patterns is large. Moreover, the time-averaged score value in combination with a statistical significance test enables different wind forecasts to be ranked by their performance.