A COMPARISON OF VERY SHORT-TERM QPF S FOR SUMMER CONVECTION OVER COMPLEX TERRAIN AREAS, WITH THE NCAR/ATEC WRF AND MM5-BASED RTFDDA SYSTEMS

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A COMPARISON OF VERY SHORT-TERM QPF S FOR SUMMER CONVECTION OVER COMPLEX TERRAIN AREAS, WITH THE NCAR/ATEC WRF AND MM5-BASED RTFDDA SYSTEMS Wei Yu, Yubao Liu, Tom Warner, Randy Bullock, Barbara Brown

1 A COMPARISON OF VERY SHORT-TERM QPF S FOR SUMMER CONVECTION OVER COMPLEX TERRAIN AREAS, WITH THE NCAR/ATEC WRF AND MM5-BASED RTFDDA SYSTEMS Wei Yu, Yubao Liu, Tom Warner, Randy Bullock, Barbara Brown and Ming Ge (National Center for Atmospheric Research, Boulder, Colorado) 1. INTRODUCTION During the last few years, NCAR and the Army Test and Evaluation Command (ATEC) developed a real-time rapid-cycling FDDA and forecast (RTFDDA) system. This MM5-based system was deployed and is running operationally at five Army test ranges. A collaborative effort was made to transition the ATEC-system core from MM5 to WRF during the past 18 months (Liu et al., 2005 and 2006; WRF-MM5 workshops). With the completion of the WRF- RTFDDA system last summer, two parallel WRF- RTFDDA and MM5-RTFDDA systems were set up and run in real-time, for systematic evaluations, at selected ATEC ranges. In this paper we describe the models verification of the precipitation analyses and a 7 hour forecast of summer convection over the complex terrain of New Mexico and Arizona. The precipitation output from the two models is verified against the NCEP STAGE IV analyses. An object-based precipitation verification method is employed to quantify the statistical performance of the two modeling systems. 2. MODELING SYSTEMS AND PARALLEL RUNS As part of the effort transitioning the ATEC RTFDDA systems from MM5 to WRF, the newly developed WRF-based RTFDDA system was set up to run in real-time, for systematic evaluation, in parallel to the MM5-based operational RTFDDA system at selected ATEC ranges. Meanwhile, the WRF-RTFDDA systems were also set up to run for retrospective cases/periods to compare WRF-RTFDDA performances for different weather regimes of special interest. Here we focus on the summer orographically forced convection events over the White Sand Missile Range (WSMR). August 2005 was chosen for the retrospective model runs. The WRF-RTFDDA system was configured to have the same domains and cycling controls as the real-time MM5- RTFDDA system running at the range. The models have three nested grids with 30, 10 and 3.33 km grid increment (Fig.1). As shown in Fig.1, the 10 km domain (D2) covers most of the southern Rocky Mountains over New Mexico and Arizona, and the finest mesh (D3) includes the main WSMR test region and two major mountains to the west and east. Both systems cycled every 3 hours. D1 D2 D3 Fig. 1 WSMR test range of MM5-RTFFDDA domain configuration Similarly, the WRF-RTFDDA model physics and vertical levels were set to be close to those of the real-time MM5-RTFDDA system. Readers can refer to Liu et al. (2005) for the model physics schemes used in the two modeling systems. Two WRF-RTFDDA runs were conducted throughout August One used the Kain-Fritch scheme on Domain 1 and 2 and the other used the Grell ensemble cumulus parameterization scheme. The model outputs were analyzed and compared to the archive of the real-time MM5-RTFDDA operated at WSMR during August In this study, 3-hr rain accumulations, ending at 00, 03, 06, 21Z were collected from the

analyses and 7-hr forecasts were collected from the WRF- and MM5-RTFDDA runs for August 2005. The rain data were then analyzed to study the day-to-day precipitation evolution.

Statistics of precipitation objects, defined by using the Brown, et al.

RESULTS During August 2005, there was frequent afternoon convection over New Mexico and Arizona in response to the diurnal solar-heating cycle.

life cycle. Convective events initiate around noon, continue to develop and reach maximum in the later afternoon, and dissipate after sunset. Fig.

2 analyses and 7-hr forecasts were collected from the WRF- and MM5-RTFDDA runs for August The rain data were then analyzed to study the day-to-day precipitation evolution. The month-long statistics were evaluated to study the overall performance of the two systems in simulating the summer convection intensity and life-cycle. Statistics of precipitation objects, defined by using the Brown, et al. (2004) approach, are computed using the NECP Stage IV analyses in order to compare the abilities of the modeling systems in simulating the observed precipitation features. 3. RESULTS During August 2005, there was frequent afternoon convection over New Mexico and Arizona in response to the diurnal solar-heating cycle. The complex terrain, particularly the numerous mountain ranges of varying sizes and orientations, were influential in triggering the convective development and subsequently controlling the convective life cycle. Convective events initiate around noon, continue to develop and reach maximum in the later afternoon, and dissipate after sunset. Fig. 2 shows the diurnal cycle of the monthly domain-average precipitation on Domains 2 and 3. The forecasts from both models nicely replicated the convection diurnal cycle observed in the Stage IV rain analyses. The precipitation begins at around 18Z, and then intensifies to a maximum at around 00Z, and decreases after that. On Domain 2, the two models have similar domain-averaged precipitation from 18Z to 03Z. However, on Domain 3, the two models are very different: MM5 overestimates the precipitation and WRF underestimates it. The daily evolution of the domain-mean precipitation shows a similar result (not shown). Fig. 2 Monthly domain averaged precipitation forecast for domain 3 and domain 2. (Left: domain 3, Right: domain 2) Fig. 3 shows the monthly-mean of 3-hr precipitation accumulations of MM5 and WRF forecasts at 00Z on Domain 2. It can be seen that both MM5 and WRF precipitation agree with the STAGE IV analysis. Two significant precipitation features are worth discussing: one is in the western domain, a precipitation belt from northwest to southeast with several isolated maxima; another is to the east of the WSMR. The MM5 forecasted stronger and smaller precipitation centers than did WRF, where MM5 is more consistent with the STAGE IV analysis. The major precipitation cores and bands in the models and observations appear to align well with the terrain distribution. Thus, both MM5 and WRF are able to simulate the terrain-forced convection. ST IV MM5 FCST Fig. 3 Monthly domain averaged precipitation forecast for domain 2 at 00Z. (top-left: STAGE IV, topright: MM5, bottom: WRF) In spite of the overall consistency, one can observe many fine-scale differences between the WRF and MM5 rain in Fig. 3. One thing worth noting is that, in the WSMR area, WRF shows a white square window with much less precipitation in the area of Domain 3. This discontinuity is an artifact. On Domain 2, the KF parameterization and Reisner s mixed-phase microphysical parameterization are used to model the moist process, while on Domain 3, only the explicit scheme is used. Thus, the window indicates that the WRF precipitation process on the coarse and fine grids (Domain 2 and Domain 3) does not match. To find out if the mismatch is due to the KF scheme, we conducted another identical WRF-RTFDDA run for the month, but

used a GF scheme for cumulus parameterization. The results from these runs show a similar phenomenon.

ST IV MM5 FCST On the finest domain (Domain 3), on which moist processes are simulated with explicit schemes (Reisner for MM5 and Lin for WRF) only, WRF-RTFDDA and MM5-RTFDDA differ greatly in terms

MM5 forecasts a larger area with more intense precipitation, which is consistent with the Stage IV analysis, although MM5 appears to overestimate the rain amount.

Nevertheless, the system obviously underestimates the rain on the rest of the domain. This result is consistent with the results shown in Fig.

3 used a GF scheme for cumulus parameterization. The results from these runs show a similar phenomenon. This grid-jump of the precipitation is also shown in the daily runs, both in the WRF- RTFDDA forecasts and analyses. ST IV MM5 FCST On the finest domain (Domain 3), on which moist processes are simulated with explicit schemes (Reisner for MM5 and Lin for WRF) only, WRF-RTFDDA and MM5-RTFDDA differ greatly in terms of both the amount and distribution of the precipitation predictions. MM5 forecasts a larger area with more intense precipitation, which is consistent with the Stage IV analysis, although MM5 appears to overestimate the rain amount. Like the MM5- RTFDDA forecasts, WRF-RTFDDA also nicely reproduces the observed rain amount in the mideastern domain where steep mountains are located. Nevertheless, the system obviously underestimates the rain on the rest of the domain. This result is consistent with the results shown in Fig. 1, where we clearly see that WRF underestimates the total precipitation in the domain. When comparing the WRF-RTFDDA analysis with the forecast (Fig. 4), it can be seen that the analysis precipitation is stronger than in the forecasts, and the overall distribution and amount of the analysis rain compares better to the STAGE IV observation. Because the differences between the model analyses and forecasts are mostly due to the nudging toward available temperature, wind and moisture observation from about 20 surface stations in the domain in the analysis, the significant differences of the precipitation between the analyses and forecasts should be caused by the PBL variation due to the surface data nudging effect. WRF ANALYSIS Fig. 4 Average precipitation of August 2005 on domain 3 at 00Z. (top left: STAGE IV, top right: MM5 forecast, bottom left: WRF forecast, bottom right: WRF analysis) Because summer convection in the region is mainly caused by solar radiation heating, the 2-m surface temperature of the WRF analysis and forecast and the MM5 forecast are compared. Fig. 5 shows the monthly-averaged 2-meter temperature of the models at 18Z, when convection begins to develop. It is obvious that the WRF-RTFDDA forecast of the low-level atmosphere is cooler than its analysis. This explains why the WRF forecast has less precipitation than its forecast. In the forecast, the low level of the model is cooler, the atmosphere is stable, the convection is less active, and thus less precipitation appeared in the 7-hr forecast. Incorporating the extra surface data, in the WRF analysis, warms up the low-level atmosphere and thus intensifies the convective development. It should be noted that the low-level temperature of the WRF analysis is still lower than in the MM5 analysis, and the MM5 analysis agrees more with the observations (not shown).

MM5 ANA WRF ANA values and then defining the attributes of the objects, including sizes, median intensities, axis orientations and others.

(top left: MM5 forecast, top right: WRF analysis, bottom: WRF forecast) A lower temperature forecast in the lower atmosphere also exists in Domain 2.

Domain 2 makes use of a cumulus parameterization and an explicit scheme. It is interesting to see that the cumulus parameterization scheme is less sensitive to the near surface cold bias.

4 MM5 ANA WRF ANA values and then defining the attributes of the objects, including sizes, median intensities, axis orientations and others. The quality of the forecasted precipitation is evaluated based on how the forecasted objects match the observed objects. Fig. 5 Average 2 meter temperature of August 2005 on domain 3 at 18 Z. (top left: MM5 forecast, top right: WRF analysis, bottom: WRF forecast) A lower temperature forecast in the lower atmosphere also exists in Domain 2. But there, the affection on the precipitation is not as obvious as in Domain 3. This could be related to the different precipitation parameterizations in Domains 2 and 3. Domain 2 makes use of a cumulus parameterization and an explicit scheme. It is interesting to see that the cumulus parameterization scheme is less sensitive to the near surface cold bias. Apparently, to improve the WRF-RTFDDA performances on the summer convection simulation, it is critical to understand and correct the WRF cold bias in the region. Fig. 6 gives an example of the object-based statistics, where the total number of precipitation objects from WRF- and MM5-RTFDDA analyses and 7-hr forecasts, MM5-RTFDDA analyses and forecasts and STAGE IV observations, for a set of varying thresholds, are compared. It can be seen that the WRF model analysis and MM5 model analysis and forecasts produced similar amounts of precipitation objects to those in the Stage IV observations, while the WRF forecast underestimated the amount by half. It is interesting to point out that, although the WRF analysis has a similar number of precipitation objects as the observation, which indicates that the WRF model picks up the terrain triggering effects on convective development, the convection is not able to develop completely due to the cooler low-level atmosphere, and thus the precipitation area is small and the rain intensity is weaker.. Number of objects MM5_ANA WRF_ANA WRF_FCST MM5_FCST Stage IV 15 Thresholds 4. VERIFICATION WITH AN OBJECT-BASED APPROACH It is well known that conventional grid-based statistical verifications are handicapped for the evaluation of precipitation forecasts from high resolution models, such as the ones used in this study. In this study we used a tool developed by NCAR/RAL (Brown et al. 2004), to compare the MM5 and WRF systems with the STAGE IV for August It is recommended that readers refer to Brown et al. (2004) for more information on this approach. Essentially, the scheme identifies precipitation objects by setting intensity threshold Fig. 6 Comparison of number of forecasted precipitation objects with those of STAGE IV observations on domain 3. As shown in Fig. 2, the difference between the domain-averaged precipitation forecast of MM5 and WRF is not big. The statistical results also show that WRF and MM5 are very similar in the overall verification of the precipitation forecast for Domain 2. This is also confirmed in Fig. 2.

5 5. CONCLUSIONS To evaluate WRF-RTFDDA s ability in forecasting summer convection over the WSMR region, where complex terrain dominates, monthlong WRF-RTFDDA runs were performed in August The WRF-RTFDDA rain analyses and forecasts were compared with those of the real-time operational MM5-RTFDDA. It was found that both MM5-RTFDDA and WRF- RTFDDA were capable of forecasting the convective rain forced by the complex topography with a model grid size of 10 km. WRF presented an obvious discontinuity in the surface precipitation between the finest grid and the coarse grid, indicating incompatible moist processes between the cumulus scheme on the coarse mesh and the explicit scheme in the fine mesh. Meteorology, 4-8 Oct, Hyannis, MA, American Meteorological Society (Boston), available at Liu, Y., A. Bourgeois, T. Warner, S. Swerdlin and J. Hacker, 2005: Implementation of Observationnudging Based FDDA into WRF for Supporting ATEC Test Operation. WRF/MM5 Users' Workshop June 27-30, Boulder, CO. The statistical analysis of the model forecasts with a monthly mean and object-based approach shows that both the WRF and MM5 forecasts captured the main features of the summer orographic convection quite well. On the fine grid, WRF appears to significantly underestimate convection, and MM5 significantly overestimates it. It is found that the less active convection in the WRF model on the fine grid is due to its obvious cool bias at the surface in the region. We are in the process of investigating the generation of a cold-bias. Acknowledgements. For this study, we received help from other NCAR/RAL colleagues. We would like to express our appreciation to Carol Park, Francois Vandenberghe, Julie Schramm, and Andrea Hahmann. 6. REFERENCES Davis, C. A., B. G. Brown, R. Bullock, M. Chapman, K. Manning, R. Morss, and A. Takacs, 2004: Verification techniques appropriate for cloud-resolving NWP models. Preprints, 16th Conference on Numerical Weather Prediction, Seattle, WA, USA, Amer. Meteor. Soc., Brown, B.G., R.R. Bullock, C.A. Davis, J. Halley Gotway, M.B. Chapman, A. Takacs, E. Gilleland, and K. Manning, 2004: New verification approaches for convective weather forecasts. Preprints, 11th Conference on Aviation, Range, and Aerospace

VERIFICATION OF HIGH RESOLUTION WRF-RTFDDA SURFACE FORECASTS OVER MOUNTAINS AND PLAINS

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,