VERIFICATION OF HIGH RESOLUTION WRF-RTFDDA SURFACE FORECASTS OVER MOUNTAINS AND PLAINS Gregory Roux, Yubao Liu, Luca Delle Monache, Rong-Shyang Sheu and Thomas T. Warner NCAR/Research Application Laboratory, Boulder, CO 80301 1. Introduction Accurate high-resolution weather analyses and forecasts are of great value for wind energy production and management. Recently, in collaboration with Xcel Energy, NCAR implemented an operational RTFDDA system over the westerncentral US states for supporting wind power forecasting (Liu et al. 2009). RTFDDA is the RealTime Four-Dimensional Data Assimilation and Forecasting system built upon WRF-ARW. It is capable of continuously collecting and ingesting diverse synoptic and asynoptic weather observations from conventional and unconventional platforms and provides continuous 4-D synthetic weather analyses, nowcasts and short-term forecasts for mesoscale regions (Liu et al, 2005 and 2006). Operational RTFDDA systems have already been implemented to support many applications in military, public and private sectors in the last ten years, providing rapidly updated, multi-scale weather analyses and forecasts with the fine-mesh domain having 0.5-3 km grid increments. Much attention has been put on the WRF model predictions in surface winds and temperature fields which can be greatly influenced by local land surface and terrain conditions. Recent studies showed a general tendency of the WRF model to overestimate the 10-m wind speed and to underestimate the 2-m daytime temperature. The comparison with the ETA model (Cheng et al., 2005) revealed that WRF produces larger 2-m temperature and dewpoint mean absolute and bias error than the Eta model, and overpredicts the 10-m wind speed. During a case study over Alaska in June 2005, Molders et al., 2008 showed that WRF could capture the temporal mean variability in wind speed although the observed values were slightly over-predicted. The presence of a consistent increase in cold bias of daytime temperature predictions (up to -2 to -4C) with time of the day until 2h before sunset was observed in Prabha et al., 2008. In the present study, high-resolution WRFRTFDDA analyses and forecasts over the Western- central United States during spring 2009 are verified against diverse surface observations provided by the Meteorological Assimilation Data Ingest System (MADIS). We focused on the systematic errors (bias) of the surface temperature and wind forecasts by the high-resolution (3.3 km grids) WRF model. The following sections describe the model configuration, the verification methods and preliminary results. Also shown is an application of a Kalman Filter based bias-correction scheme and some encouraging results. 2. Model configuration The WRF-RTFDDA system is configured with three one-way nested domains with horizontal-grid increments of 30, 10 and 3.3 km. The fine-mesh domain has an area of 3,355,800 km2 with 541x571 points, covering the central part of the United States, from the Rocky Mountains to the western Midwestern States, including most of the Central/South plains (Figure 1). The system runs at 3 hour cycles with a continuous data assimilation from one cycle to the next. In each cycle, the model produces 3-4 hour FDDA analyses and 24 hour forecasts for all domains and 72 hour forecasts for the two coarser domains. The 1-degree GFS model output is used for deriving initial and lateral boundary conditions. The model has 37 vertical levels. The model physics is similar to the one used for the operational ATEC-ranges: microphysics follows Lin et al. scheme; YSU scheme is used for the PBL parameterization and Monin-Obukhov scheme for surface layer; Kain-Fritch cumulus scheme is used for the coarser domain, and convection is treated explicitly for domain 3; the Noah land-surface model is used with 4 soil layers. The system assimilates synoptic and asynoptic weather observations provided by diverse platforms: i.e. surface, upper air, ship and buoy observations from MADIS; NOAA/NESDIS satellite winds; ACARS, AMDAR; profilers observations; QuikScat sea surface winds. The archive of the real-time WRF-RTFDDA test analyses and forecasts from May 3 to June 6, 2009
are verified against surface observations provided by MADIS (Meteorological Assimilation Data Ingest System). Only MADIS surface data within the domain 3 (assimilated within RTFDDA) that reported at least 50% of the expected observations in the 35 days period were used for the verification. Based on this criterion, the total number of stations is 3550. For the purpose of the verification, 2-m temperature and 10-m wind forecast from the RTFDDA fine-mesh system were interpolated to the MADIS observation stations. after the start of the forecast (3h-5h), which could be due to model difficulties to predict the nighttime temperatures. As illustrated in Figure 2c, the model bias varies significantly from day to day during the month of May: while most days display a strong cold bias towards the end of the forecast. On 22-25 May we can notice a positive bias of +1C. The large dayby-day differences of the bias suggest that model bias in temperature greatly affected by weather regimes. Figure 3 displays the spatial distribution of the calculated monthly mean bias over the domain 3. It can be seen that the spatial spread is also strong from east to west of the domain with a cold biases (up to -4C) over the Rocky Mountains and warm biases (up to 2C) over the South plains. Results for the 10-m wind speed indicate the model tendency to over-predict the wind speed by up to 1m/s on average, with a high spread in results over the month and stations (greater than 2 m/s). As shown in Figs. 2d and 2f, this spread depends on the time of day, day in month (between -0.5 m/s and 1.5 m/s) and station location (> 5 m/s over the Rocky Mountains and <-1m/s over the plains). Figure 1: WRF-RTFDDA model domains for operational wind prediction for Xcel Engergy 3. Results and Discussion 3.1. Analysis of the overall model bias In this section we analyze systematic errors (biases) of the high-resolution (3.3 km grids) WRF model forecasts of the surface temperature and wind speed. Here we consider only one forecast cycle, in which data are assimilated during first 3 hours of the model run and then the model produce 24h forecast. Figure 2 shows the temporal evolution of the domain-averaged monthly mean bias as a function of the forecast hour (2a and 2d), as well as the day of the month (2c and 2f). For the 2-m temperature, model displays a relatively moderate cold bias with values ranging between 0C and -0.5C (Fig. 2a). However, the spread around the mean value is significant, with the deviation from averaged values that can reach +/2C (Fig 2a and 2b). As the forecast progresses, the cold bias seems to become larger (particularly visible for the lower values of the variability interval). We can also clearly see a dip in the bias curve shortly The analyses of the May 2009 averaged bias show an under-prediction for 2-m temperature and an average over-prediction for 10-m wind speed, but with a high dependence on the diurnal cycle, station location, forecast hour and weather regime. The sensitivity to each of these parameters will be discussed in the next sections. 3.2. Diurnal evolution To quantify the diurnal cycle on the averaged bias, we processed all eight RTFDDA cycles for one forecast hour. Figure 4 shows the averaged bias during the month of May for a 23h forecast. In May, the sunrise is around 6am local time and the sunset at 8pm local time (for CST area, night time is between 1AM and 11AM). On average, the 2m-temperature bias varies between 0 and -0.4C, with a colder bias during nighttime: the cold bias increases during the afternoon, with a maximum a few hours before sunset, then the cold bias persist during night time and go back to 0C a few hours before sunset. The effect of the diurnal cycle can be very high, with a cold bias down to -2C during evening and night and 0.4C during the morning (Figure 2c and 4). For 10-m wind speed, the bias is close to 1 m/s during night time and around 0.25 m/s during day, with variation around sunset and sunrise. This difference can reach 2m/s during the night and 0.2 m/s during day time.
(c) (d) (e) (f) Figure 2: Temporal evolution of the domain-averaged monthly mean bias as a function of the forecast hour for temperature and wind speed (d), as well as the day of the month (c) and (f) and the bias repartition and (e). Figure 3: Spatial distribution of the monthly mean bias for wind speed and temperature.
mountain stations. During nighttime, the model under-estimates temperatures for stations lower than 700 m and overestimates mountain stations. The highest errors are found for high mountain stations during daytime and near sea-level stations during nighttime. (c) Figure 4: Averaged bias for the month of May2009 for a 23h forecast for temperature and wind speed 3.3. Sensitivity to the terrain heights The altitude of 3550 stations used in this study varies from 35m to 4300m, with a median elevation around 685m. To investigate the mountain effect on the model errors, stations were grouped according to their elevation. Each group contains at least 400 stations. Fig. 5 presents the average wind-speed bias (for a 24-hour diurnal cycle average, fig 5a) and temperature bias (for a night average, Fig 2b and a day average, Fig 2c) as a function of the terrain elevation. Significant differences in bias can be seen between different altitude groups, and ranges from -1C to 0.5C for temperature and from -0.1 m/s to 1.1 m/s for wind-speed. For wind speed, the bias decreases with station elevation between 0 and 700 m and increases with station elevation greater than 700 m. The lowest errors are for stations between 400 and 1500 m height, and the highest for stations near sea-level and high-altitude mountain stations. For temperature, we divided the day between night time and day time, as the bias evolution differs greatly over the diurnal cycle. As expected, during day time, the model cold bias increases with station elevation, with highest errors found for high-altitude Figure 5: Average wind speed, daytime temperature and nighttime temperature (c) bias as a function of the terrain elevation. The red lines are the averaged bias for all stations higher than the elevation threshold. 3.4. Variation with the weather regimes case study of 13 and 30 May As discussed in section 3.1, the model bias appears strongly influenced by meteorological conditions as shown by large differences in bias among various days under study. This dependence is particularly well illustrated on May 13th (Figure 2f), when a frontal perturbation was approaching the northeastern part of the domain. The front was associated with strong surface winds, and precipitations, whose intensity and geographical location were not accurately captured by the model. Indeed, along the front line, we can clearly see a significant enhancement in model bias with values exceeding 5-6m/s (Fig 6a). Another example of a weather dependent model bias was observed on May 30. This day was characterized by convective condition associated with
high surface temperature in the East of the domain. Over this region, the WRF cold bias reaches -4C. We are in the process of analyzing the model output attempting to understand the driving factors for these large biases. The result demonstrates a great benefit of using this statistical scheme. It reduces an overall bias for May 2009 from -0.33C to -0.01C for temperature and from 0.46 m/s to 0.14 m/s for wind speed. As shown in figure 7 and, this bias reduction is due both to the bias correction in space and time. This method also improved the overall RMS errors and increased the model-observation correlation and the wind speed spread as shown in the Taylor diagrams (Fig 7 c). Figure 6: Spatial distribution of the wind speed bias for May 13th at 00 UTC and of the temperature bias for May 31 at 00UTC. (c) 4. Post-processing with a Kalman Filter Bias Correction Systematic forecast errors can be corrected. Here we tested the application of a Kalman Filter based bias correction algorithm to this model dataset. The Kalman filter (KF) is a recursive algorithm to estimate a signal from noisy measurements. It is applied in predictor mode, to post-process temperature and wind speed forecasts at each observation station site to remove systematic errors. Detailed description of the Kalman Filter bias correction scheme can be found in Delle Monache et al. (2006 and 2007). Figure 7: Spatial distribution of wind speed bias and temporal evolution of temperature bias for Kalman filtered data. (c) displays the Taylor diagrams for temperature and wind speed for raw data and KF data.
4. Summary The present work summarizes preliminaryverification results of 2-m temperature and 10-m wind speed of the test analyses and forecasts of the NCAR-Xcel WRF-RTFDDA model system during May 2009 against MADIS surface observations over the Western-central United States. The verification results show that the model under-predicts the 2-m temperature by -0.33C on average and overpredicts the 10-m wind speed by +0.46 m/s. Further analyses exposed that the errors are highly dependent on the diurnal cycle, station locations, forecast ranges and weather regimes. The highest errors are found for highaltitude mountains stations and near sea-level stations (up to 1 m/s for wind-speed and -1C for temperature). For temperature, the cold bias is higher a few hours before sunset and for windspeed during night-time. Weather regimes can also greatly influence model performance and on certain days the model bias can go up to 5 m/s for wind speeds and -4C for temperatures. The verification results suggest a need for studying model physics scheme deficiencies and improvements for scenario-based weather processes. Meanwhile, an application of a Kalman-Filter based bias-correction scheme can be used to effectively reduce the model bias in the model forecasts. 5. References Delle Monache, L., T. Nipen, X. Deng, Y. Zhou, and R. Stull (2006), Ozone ensemble forecasts: 2. A Kalman filter predictor bias correction, J. Geophys. Res., 111, D05308, doi:10.1029/2005jd006311. Delle Monache, L. D., Wilczak, S. McKeen, G. Grell, M. Pagowski,, S. Peckham, R. Stoll, J. McHenry, and J. McQueen, 2007: A Kalman-filter bias correction method applied to deterministic, ensemble averaged, and probabilistic forecasts of surface ozone./. Tellus, 60, doi: 10.1111/j.1600-0889.2007.00332.x/ Cheng, W.Y.Y., and W.J. Steenburgh, 2005: Evaluation of Surface Sensible Weather Forecasts by the WRF and the Eta Models over the Western United States. Wea. Forecasting, 20, 812 821. Liu, Y., and co-authors, 2005: Implementation of observation-nudging based FDDA into WRF for supporting ATEC test operations. 2005 WRF Users Workshop, Boulder, Colorado, June, 2005. Liu, Y., F. Chen, T. Warner, and J. Basara, 2006: Verification of a mesoscale data-assimilation and forecasting system for the Oklahoma City area during the Joint Urban 2003 Field Project. J. Appl. Meteor. and Climatol., 45, 912-929. Liu, Y., T. Warner, W. Wu, G. Roux, W. Cheng, Y. Liu, F. Chen, L. Delle Monache, W. Mahoney and S. Swerdlin, 2009: A versatile WRF and MM5-based weather analysis and forecasting system for supporting wind energy prediction. 23rd WAF / 19th NWP Conf., AMS, Omaha, NE. June 1-5, 2009. Paper 17B.3. Storm B, Dudhia J, Basu S, Swift A,Giammanco I, 2009: Evaluation of the Weather Research and Forecasting model on forecasting low-level jets: implications for wind energy. Wind Energy 12(1): 81. Molders N, 2008: Suitability of the Weather Research and Forecasting (WRF) Model to Predict the June 2005 Fire Weather for Interior Alaska. Weather and Forecasting 23(5): 953. Prabha T, Hoogenboom G, 2008: Evaluation of the Weather Research and Forecasting model for two frost events Computers and electronics in Agriculture 64 (2), 234-247.