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Description
The .tgz files contain NetCDF files of atmospheric river (AR) objects as processed through the Method for Object-based Diagnostic Evaluation (MODE; Bullock et al. 2016) software. MODE is part of the Model Evaluation Tools (MET; https://dtcenter.org/community-code/model-evaluation-tools-met) package provided by the National Center for Atmospheric Research (NCAR). ARs are narrow areas of intense moisture transport that can be identified using gridded fields of integrated vapor transport (IVT) from a numerical weather prediction model and/or analysis (described below). Objects are used to evaluate the forecast skill of the AR over an 11 year (2006-2017) period during the cool season (Dec-Mar). AR objects are defined based on an IVT threshold (250, 500, 750, 1000 kg m-1 s-1), length, and location providing a well-defined AR region with a geographical footprint for both a forecast and an analysis. Fixed thresholds are used to determine the boundary of the AR object applied to evaluate different strengths of AR intensity. Length restrictions for the AR objects are based on IVT threshold: At 250 kg m-1s-1 the minimum required length to detect an AR is 2000 km, at 500 kg m-1s-1 it is 1500 km, at 750 kg m-1s-1 it is 1000 km, and at 1000 kg m-1s-1 it is 550 km. At all thresholds, the centroid of the object is required to be north of 24o latitude (to exclude tropical areas of high IVT) and east of 205o (to exclude ARs near the western boundary of our domain). MODE uses convolution thresholding as described in Bullock et al. (2016), which has two tunable parameters: threshold (as discussed above) and convolution radius. This is done independently for a forecast and for a verification (analysis). After AR objects have been identified for both the forecast and analysis, MODE determines if the forecasted AR matches the analysis AR (is a “hit”) using fuzzy logic, with tunable weights for different attributes. We have given the distance between the boundaries of the two objects the strongest weight in determining a match between a forecast and analysis. The distance between the centroids of the objects and the size of the objects are the next most important factors, each with half the weight of the boundary distance. Other object attributes have smaller weights. If the forecasted AR meets the criteria for matching the analysis AR, then statistics are computed comparing the two objects. This analysis evaluated two numerical weather prediction datasets (coarse and fine scale) against ground truth. The coarser model IVT was calculated from the control member from the Global Ensemble Forecast System (GEFS) reforecast (Zhou et al. 2017). The IVT is calculated from the half degree resolution using 33 pressure levels from 1000 hPa to 200 hPa. The fine scale IVT was calculated from a dynamically downscaled GEFS forecast using the regional Weather Research and Forecasting (WRF) model (Skamarock et al., 2008). This model is run at a 9 km resolution over the Eastern Pacific and extreme U.S. West Coast. The configuration of the WRF model is primarily based upon Martin et. al 2018, with the exceptions of a larger domain (see domain extent) and increased vertical levels (60). Atmospheric River Program Phase 2-202203. Award #4600013361 Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) DeHaan, Laurel L.; Martin, Andrew C.; Weihs, Rachel R.; Delle Monache, Luca; Ralph, F. Martin (2021). Western Pacific Atmospheric River Object Data from MODE detection software using West-WRF, GEFS, and MERRA-2. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0W37TVB The files are subsetted based on IVT threshold and model (GEFS, WRF). Each file contains 1320 valid dates (yyyymmddhh) where the 00Z forecast is only analyzed. Each valid time contains the following types of files: Attributes files ([…]_obj.txt) Netcdf Objects file ([…]_obj.nc) Contingency Table Counts and Stats file ([…]_cts.txt) PostScript Graphics file ([…]_.ps) Please refer to the MET guide for explanations of each of these file types (https://met.readthedocs.io/en/latest/) The direct output files from MODE are organized using the following naming architecture: mode_$MODEL_$OBSERVATION_$FORECASTLEADTIME_$VALIDDATE_$VALIDHOURFCST_$VALIDHOUROBS Where $MODEL = either GFS or WRF $OBSERVATION = analysis used (MERRA-2) $FORECASTLEAD TIME = 24, 48, 72, 96, 120-hour forecast (appended by zeros) $VALIDDATE = yyyymmdd $VALIDHOURFCST = time (Z) in which forecast is valid $VALIDHOUROBS = time (Z) in which observation is valid Additionally, several other post-processed files are produced using the MET output for the model evaluation. The files named : 90thP_$MODEL_$OBSERVATION_$VALIDDATE$VALIDHOURFCST$VALIDHOUR.txt have the 90th percentile of the IVT values within an object extracted from the MODE output. The first value in the file is for the observation, followed by the forecast values for the 24 hour forecast to the 168 hour forecast. The files named: areaM_$MODEL_$OBSERVATION_$VALIDDATE$VALIDHOURFCST$VALIDHOUR.txt have area values extracted from the MODE output. The columns are for the forecasts from 24 hours to 168 hours. The first line contains the intersection between the forecast area and observed area; the second line contains the area of the observation; and the third line contains the area of the forecast. The files names landfall_maxIVT_$VALIDDATE$VALIDHOURFCST$VALIDHOUR.txt contain information about the landfalling location of the object, with the columns referring to forecasts from 24 hours to 168 hours. The first row has the observed latitude of maximum IVT for an object crossing the coastline. The second row has the same for the forecasts. The third row indicates is a forecast is a hit (both the forecast and observation have landfalling objects), the 4th row indicates if the forecast is a miss, and the 5th row indicates if the forecast is a false alarm.
Type
dataset
Identifier
ark:/20775/bb0759039x
Language
English
Subject
Integrated vapor transport (IVT) Method for Object-based Diagnostic Evaluation (MODE) Forecast skill Global Ensemble Forecast System (GEFS) Weather Research and Forecasting (WRF) Atmospheric river detection Numerical weather prediction Atmospheric river Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)
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