Regional Deterministic Prediction System (RDPS) Technical Note

Transcription

1 Regional Deterministic Prediction System (RDPS) Technical Note Last update: November 18, 2014 from version to version

2 Version Date Author Variation or revocation /10/2014 J.-F. Caron Creation of document First version of content for Sections 1 to /10/2014 D. Anselmo Addition of text covering UMOS, SCRIBE, the subjective evaluation, and dependent sub-systems /11/2014 J.-F. Caron Some corrections /11/2014 D. Anselmo Final modifications before publishing. 2

3 Table of Contents Summary... 4 Nomenclature Introduction Forecast model configurations and cycling strategies Changes to the data assimilation system Changes to the forecast models Objective evaluation of the final R&D test series Evaluation of the parallel run Performance of dependent systems Availability of products Summary of the results Acknowledgements References

4 Changes to the Regional Deterministic Prediction System (RDPS) from version to version Research, Development, and Operations Divisions at the Canadian Meteorological Centre, Environment Canada Summary The modifications to the data assimilation component of the regional deterministic prediction system (RDPS) implemented at Environment Canada operations during the fall of 2014 are described. The main change is the replacement of the limited-area 4DVar data assimilation algorithm for the limited-area analysis and the associated 3DVar scheme for the synchronous global driver analysis by the 4D ensemble-variational (4DEnVar) scheme presented in the companion TechNote (GDPS-TN) for the global deterministic prediction system (GDPS). Further forecast improvements were also made possible by upgrades in the assimilated observational data and by introducing the improved global analysis presented in GDPS-TN in the RDPS intermittent cycling strategy. The computational savings brought about by the 4DEnVar approach are also discussed. 4

5 Nomenclature 3DVar: Three-dimensional variational data assimilation 4DVar: Four-dimensional variational data assimilation 3DEnVar: Three-dimensional ensemble-variational data assimilation 4DEnVar: Four-dimensional ensemble-variational data assimilation AD: Adjoint CMC: Canadian Meteorological Centre EC: Environment Canada ECMWF European Centre for Medium-Range Weather Forecasts EnKF: Ensemble Kalman Filter GB-GPS ZTD: Ground-based global positioning system zenith tropospheric delay GDPS Global Deterministic Prediction System GEM: Global Environmental Multi-scale model ETS: Equitable threat score LAM: Limited area model LBC: Lateral Boundary Condition RDPS: Regional Deterministic Prediction System SCRIBE: EC s software to assist in the preparation of forecast bulletins TL: Tangent-linear UMOS: Updateable Model Output Statistics 5

6 1. Introduction In the companion technical note (Buehner et al. 2014; hereafter referred to as GDPS-TN), the latest modifications to the global deterministic prediction system (GDPS) implemented operationally at Environment Canada (EC) in the fall of 2014 were described. The most notable change is the replacement of the four-dimensional variational data assimilation (4DVar) scheme by a fourdimensional ensemble-variational (4DEnVar) scheme in which the background-error covariances are represented by a blend of climatological covariances and 4D flow-dependent covariances derived from a global ensemble Kalman filter (EnKF). In this document we report on the implementation of the same 4DEnVar scheme in the regional deterministic prediction system (RDPS). Further forecast improvements were obtained from changes to the assimilated observational data and the introduction of the improved global analysis presented in GDPS-TN in the RDPS intermittent cycling strategy. Similarly to global data assimilation applications, considerable effort has been devoted in recent years to both comparing and combining ensemble-based and variational-based limited-area data assimilation approaches (Wang et al. 2008a,b; Zhang and Zhang 2012; Zhang et al. 2013; Liu and Xiao 2013; Schwartz and Liu 2014; Gustafsson and Bojarova 2014, Pan et al. 2014). As reported by Zhang and Zhang (2012), using 4DVar in combination with an ensemble-derived covariance matrix (hybrid 4DVar) can outperform a stand-alone EnKF or a 4DVar in a limited-area forecasting system, in agreement with the results from Buehner et al. (2010) in a global configuration. However, the latter approach still requires the use of tangent-linear (TL) and adjoint (AD) versions of the forecast model, whose time integrations dominate the cost of the analysis step and which requires, moreover, significant development and maintenance effort. On the other hand, as explained in Buehner et al. (2013; hereafter B13), 4DEnVar uses 4D ensemble covariances in a way that essentially replaces the use of TL and AD versions of the forecast model and has been shown to produce forecasts with similar or improved accuracy at short lead times in a global context by B13 and in GDPS-TN of this paper. Recently Gustafsson and Bojarova (2014) reported that 4DEnVar can outperform both 4DVar and hybrid 4DVar in the context of the limited-area HIRLAM forecasting system. Therefore, the 4DEnVar approach described in GDPS-TN seemed appropriate for the RDPS in order to 1) improve, or at least maintain, the RDPS forecast accuracy obtained using the operational limited-area 4DVar scheme and 2) make more efficient use of resources at EC by moving towards a more unified data assimilation approach for deterministic and ensemble forecasting. Since there is currently no operational equivalent to the global EnKF (Houtekamer et al. 2014) at the regional scale at EC, we simply based our 4DEnVar scheme for the RDPS limited-area analysis on the use of 4D ensemble covariances derived from the global EnKF as in the GDPS configuration described in GDPS-TN. Our approach is thus similar to the National Centers for Environmental Prediction (NCEP), which recently replaced the 3DVar scheme in the North American Mesoscale (NAM) and the Rapid Refresh (RAP) regional forecasting systems by a 3DEnVar scheme based on their global EnKF system (see NWS 2014a,b). 6

7 2. Forecast model configurations and cycling strategies The forecast model configurations and the cycling strategy of the RDPS remain the same (except for one bug correction in the models explained in section 4) in v Nevertheless they are summarized again here to facilitate the comprehension of this document. The RDPS represents the main NWP guidance for forecasters of the Meteorological Service of Canada for day one and two. It produces forecasts over North America (see again Fig. 1) up to T + 48 h (T + 54 h) at 00 and 12 UTC (06 and 18 UTC) using a limited-area (LAM) version of the GEM model (Côté et al. 1998) on a cylindrical equidistant grid with a horizontal grid spacing of 0.09 o (approximately 10 km) and 80 vertical levels (model lid at 0.1 hpa, as in the GDPS). Unlike the GDPS, the RDPS still uses a hydrostatic-pressure coordinate (defined in Charron et al. 2012) and a discretization on a regular (unstaggered) vertical grid, due to compatibility issues between some physical process parameterizations used in the RDPS and the log-hydrostatic pressure vertical coordinate defined on a staggered grid proposed by Girard et al. (2014). Figure 1. Map showing the domain of the limited-area model in the RDPS using an approximate 10 km horizontal grid spacing. This forecasting system employs an intermittent upper air cycling strategy (Fig. 2) where the (approximately 25 km) analysis from the GDPS serves to initialize the 6 h LAM forecast before the analysis time T (named LB in Fig. 2). This forecast serves as the background state for the analysis step at time T (named LF in Fig. 2). In parallel, the same procedure is applied to a global GEM driving model with a horizontal grid spacing of approximately 33 km. The resulting synchronous global driving analysis (named DF in Fig. 2) and forecast, as proposed and tested by Fillion et al. (2010; hereafter F10), allows the observations outside the LAM analysis domain to influence the LAM forecasts through the lateral boundary conditions (LBCs). 7

8 Figure 2. Schematic of the RDPS intermittent upper air cycling strategy showing its dependency on the GDPS. The distances refer to the approximate horizontal grid spacing of the nonlinear models. See text in section 2 for further details. The driving model uses the same vertical levels as the LAM in order to minimize interpolations in the prescription of the LBCs, whose formulation in the LAM version of GEM is based on Thomas et al. (1998). In each forecast a digital filter procedure (Fillion et al. 1995) is applied to eliminate the spurious gravity waves triggered by imbalances in the initial conditions. Interested readers are referred to CMC (2012) and references therein for further details on the model configuration used in the RDPS, especially regarding the parameterization of the physical processes. Both the LAM and the driving component use the Interactions between Soil Biosphere Atmosphere (ISBA) land surface scheme, which has its own data assimilation and cycling strategy (see Bélair et al. 2003a,b for complete details). In the driver, the surface variables from the GDPS at T 6 h are used to initialize the DB forecast (see Fig. 2) and no update of these variables is made in DF at time T (i.e., DF starts from 6 h forecast surface variables). The LAM component uses a continuous data assimilation cycling strategy where surface temperatures and soil moisture contents are updated every 24 h at 00 UTC. LAM forecasts (LB and LF) initialized at 06, 12, and 18 UTC simply rely on the forecast surface variables from the most recent 00 UTC surface analysis. 3. Changes to the data assimilation system 3.1. From 4D/3DVar to 4DEnVar The previously operational 4DVar scheme used to perform the LAM analysis is based on the formulation described in T12 and is a temporal extension of the 3DVar scheme proposed by F10. It operates with an incremental formulation (Courtier et al. 1994) to produce analysis increments on a 8

9 global Gaussian grid (i.e. with a horizontal grid spacing of approximately 100 km). The homogeneous and isotropic background-error covariances used at the start of the assimilation window (T 3 h) are defined with a global spherical-harmonics spectral representation, but the TL and AD models are defined over a limited-area domain exceeding (by 30% in each direction) the horizontal grid of the high-resolution (~10 km) nonlinear model (see Fig. 5 in T12). Because the TL and AD model grid chosen is an exact sub-domain of the global Gaussian analysis grid, the communication of information (i.e., analysis increments and adjoint sensitivities) is direct, with no spatial interpolations required. This configuration mimics the solution of a global 4DVar (but at a reduced computational cost) in order to prevent distortions in the analysis increments near the lateral boundaries of the nonlinear model and to optimize the analysis of the large scales in this continental-scale LAM. The TL and AD models used here include the same dynamical processes as the high resolution forecast model, but with only two physical processes: simplified vertical diffusion and simplified grid-scale condensation. Only one outer loop with 45 iterations is performed, and the resulting global analysis increments at low resolution (~100 km) are then interpolated to the high resolution LAM grid (~10 km) and added to the background state at T 3 h. A 3 h nonlinear forecast is then needed to carry this analysis to time T. See T12 and F10 for further details. The global driver analysis, on the other hand, is obtained from a 3DVar system since a global 4DVar system would require too much time to meet EC's operational constraints for RDPS products. The analysis increments are also produced on a global (~100 km) Gaussian grid, but using the same background-error covariances employed by the global 4DVar in the previously operational version of the GDPS (see Charron et al. 2012) and with all the observational data available over the entire globe. The 4DEnVar scheme tested and adopted in the RDPS is identical to the system implemented in the GDPS and presented in section 2a of GDPS-TN and in B13. Using an analysis grid that matches the horizontal grid spacing of the 4D background-error covariances obtained from the global EnKF (~66 km in section 3 and 4; ~50 km in the final configuration presented in section 5), it is possible to obtain analysis increments at a higher resolution than the limited-area 4DVar scheme presented above. Because no TL or AD model integrations are necessary in 4DEnVar, the computational cost of performing a LAM analysis at a higher resolution using the ensemble covariances from the global EnKF is still lower than the limited-area 4DVar scheme. Moreover, the computational efficiency of the 4DEnVar makes it possible to use this approach for the global driver analysis. A comparison of timings and computational resources between the different data assimilation approaches is presented in section 5d. As in GDPS-TN, the ensemble-derived background-error covariances are blended with the same homogeneous and isotropic background-error covariances used in the GDPS for each analysis 9

10 (LAM and global driver) using the same vertical profile of weighting factors and the same spatial localization design. Unlike the GDPS, the RDPS based on 4DEnVar still uses a cold start strategy and a non-incremental digital filter initialization procedure in forecasts initialized at time T, simply because the incremental analysis update and the physics recycling (warm start) capability now used in the GDPS (see Section 2g in GDPS-TN) are not available in the older version of the GEM model used in the LAM and the driver components. Therefore, it was decided to select the 4DEnVar analysis increment valid at time T obtained after 50 (70) iterations in the LAM (driver) analysis, which further reduces the computational cost compared to 4DVar, since no forecast step is needed to carry the analysis from T 3 h to T Changes to the observational data assimilated The various improvements to the processing and the volume of data assimilated in the GDPS described in section 2 of GDPS-TN were also implemented in the RDPS. In short, the most important changes are: 1) coefficients from a revised satellite radiance bias correction scheme were implemented 1, 2) the processing and the assimilation of radiosonde and aircraft data were upgraded (this includes introducing a bias correction strategy for aircraft temperature data and taking into account the horizontal drift of radiosonde balloons as in Laroche and Sarazin, 2013), 3) the number of assimilated channels for AIRS and IASI radiances was increased and, finally, 4) zenith tropospheric delay (ZTD) data from ground-based GPS (GB-GPS) receivers (sensitive to precipitable water) over North America are now assimilated. See Sections 2b to 2f and Tables 2 and 3 in GDPS-TN for complete details Computational resources The forecast improvements in v are also associated with a major reduction in the computational cost of the analysis steps in the RDPS. Table 2 shows the average computational resources of the analysis steps (LAM and global driver) for v and v.3.2.1, where the computational cost is defined in terms of the product between the number of CPUs used and the elapsed wall-clock time. In the LAM component, the replacement of the 4DVar reduces the computational cost by an order of magnitude due to significant reductions in both the required number of CPUs (320 vs 2048) and in the wall clock time (7 min vs. 17 min). This is largely the result of replacing the integration of the TL and AD models in 4DVar by the use of an ensemble of nonlinear model states to estimate 4D background-error covariances over the assimilation time window. However, it is important to stress that 4DEnVar analyses are generated using an updated version of the variational analysis program, and benefit from improvements in the optimization of the FORTRAN code, especially from a better usage of the Message Passage Interface (MPI) protocol. Note that the computational cost of 4DVar does not include the 3 h nonlinear model forecast step (3 min using 1024 CPUs) to carry the analysis increments from T 3 h to the analysis 1 No bias correction coefficients are computed in the RDPS. The coefficients are simply taken from the GDPS. 10

11 time T, which is not needed in 4DEnVar. In the global driver, in order to maintain a wall clock time similar to that in the 3DVar analysis in v (i.e., 9 min), the 4DEnVar component in V demands more CPUs (320 vs. 64) and therefore shows an increased cost (see again Table 2). Nevertheless, the total cost of the two analyses in V remains well below the total cost in v TABLE 2. Comparison of averaged computational resources used for the analysis steps of the RDPS in experiments v and v on an IBM Power 7 system. The wall clock time represent the elapsed time between the start of the variational analysis program and the end of the writing of the analysis increments. The cost is simply defined as the product of the wall clock time and the number of cores used. The cost of the nonlinear forecast from T 3 h to T in 4DVar is not included. cores Wall clock (min) Cost (core min) LAM v (4DVar) v (4DEnVar) Global driver v (3DVar) v (4DEnVar) Changes to the forecast models As in the GDPS v.4.0.0, a unit conversion error in the formulation of the prognostic equations for the snow canopy density was corrected in the model version used for the LAM and for the driving forecasts in v.4.0.0, leading to an approximate doubling of the snow density seen by the models. This correction in the model formulation had a significant impact on the near-surface temperature in winter, as will be shown later in Section Objective evaluation of the final R&D test series For each winter (February and March) and summer (July and August) period of 2011, a total of h forecasts were initialized at 00 and 12 UTC. In order to measure the full impact of the changes, 11

12 the surface variable cycling strategy in the LAM (see Section 2.1) was activated in both the new configuration (v.4.0.0) and in the control experiment (v.3.2.1). The only difference between the final series and the parallel run performed during the Summer and Fall 2014,with the exception of the time period, is that the assimilated observations were obtained using a data cutoff time of T + 9h instead of the data cutoff time used at EC operations: T + 2h. The results reported below show the impact on forecasts from all the updates in the RDPS. Readers interested in the impact on forecasts from partial updates (e.g. replacing only 4DVar by 4DEnVar) are referred to Caron et al. (2014) Upper air verification To be consistent with the assimilation of radiosonde data at their reported or estimated geographical position during the balloon ascent in v.4.0.0, forecasts from both v and v where verified against radiosonde data by taking into account the horizontal drift. However, as in Laroche and Sarazin (2013), all the observations used for the verification were assumed to be valid at the synoptic time (00 or 12 UTC) despite that reported and estimated observation times were taken into account in the data assimilation step of v This verification strategy is also used in Section 6.1. Figure 3 and Figure 4 show the verification for the winter period while Figure 5 and Figure 6 show the results during the summer period. Forecasts from v better match radiosonde observations at most vertical levels compared to v for both winter and summer periods. Figure 7 and Figure 8 show the fit of the analyses to the observations. It can be seen that the 4DEnVar-based analyses are closer to radiosonde observations than the 4DVar-based analyses, but this DOES NOT necessarily mean that 4DEnVar-based analysis are closer to the true state of the atmosphere. Independent observations (observations that were not assimilated in either 4DEnVar or 4DVar) would be needed to estimate the accuracy of the analyses. However, based on the forecast improvements observed above and below, we can infer that 4DEnVar-based analyses represent an improved estimate of the true state of the atmosphere compared to 4DVar-based analyses. 12

13 Figure 3. Final series verification for 24 h forecasts from v (red) and v (blue) against North American radiosondes. The standard deviation (solid lines) and bias (observation minus forecast; dashed lines) are shown for zonal wind (UU; upper left panels), wind module (UV; upper right panel), geopotential height (GZ; middle left panel), temperature (TT; middle right panel), and dew point depression (ES; lower left panel). Scores are averaged over 118 winter cases in Boxes on the left (right) of the figures indicate statistical significance levels for biases (standard deviation). Red (blue) boxes indicate that the v (v.3.0.0) experiment is better. No box indicates that the null hypothesis (that statistics of the two samples are the same) cannot be rejected. The radiosondes horizontal drift during balloon ascent was taken into account in order to be consistent with the changes introduced in the data assimilated in v

14 Figure 4. As in Fig. 3, but for 48 h forecasts. 14

15 Figure 5. As in Fig. 3, but for 118 summer cases in

16 Figure 6. As in Fig. 3, but for 118 summer cases in 2011 and for 48 h forecasts. 16

17 Figure 7. As in Fig. 3, but for 00 h forecasts (analyses). 17

18 Figure 8. As in Fig. 3, but for 118 summer cases in 2011 and for 00 h forecasts (analyses). 18

19 5.2. Ground-based GPS Figure 9 shows the verification of precipitable water forecasts against the values derived from GB- GPS ZTD observations. An improved fit can be seen at every lead time during the summer period. These improvements are largely due to the addition of GB-GPS ZTD observations in the data assimilated in both the RDPS and the GDPS in v and are in agreement with the findings of Macpherson et al. (2008). In the winter period, when precipitable water is very low, no change was observed in the verification scores (not shown). Figure 9. Final series verification of forecasts from v (red) and v (blue) against precipitable water (mm) derived from GB-GPS ZTD observations over North America as a function of lead times (h) for the summer period. The standard deviations (biases; forecast minus observation) are shown as solid (dashed) lines. Scores are averaged over 118 cases and the error bars represent a 90% confidence interval. 19

20 5.3. Surface verification The forecasts were also verified against METAR (aviation routine weather report) and synoptic observation data available operationally at EC as in Wilson and Vallée (2003) using the USTAT verification package. Only reports from surface-based stations with an elevation difference with the LAM topography of less than 100 meters were used, which led to a total of 1282 stations being considered: 984 in Canada and 210 in the US. Therefore the verification scores presented here are more representative of the forecast performance over Canada. Statistics were partitioned between forecasts initialized at 00 UTC and 12 UTC in order to capture the different behavior during daytime and nighttime regimes. Figure 10 and Figure 11 show the verification for screen level temperature and dew point, respectively, for the summer period while Figure 12 and 13 show the scores for the winter period. While some improvements can be seen in summer, the largest improvements can be seen in winter due to the bug correction in the prognostic equations of the snow canopy density in the model formulation. Precipitation accumulations were also verified using the equitable threat score (ETS; see e.g., Mason 2003) for 5 thresholds ( 0.5, 2, 5, 10, and 15 mm) of 12 h accumulations from synoptic reports. The scores are reported in Figure 14 (for summer) and Figure 15 (for winter). Again, some improvements in v compared to v can be seen at most of the forecast lead times in each season, although many are barely statistically significant. 20

21 K K 1.5m T Summer 00 UTC 12 UTC a) Std Dev b) Std Dev c) Bias d) Bias Lead Time (hours) Lead Time (hours) Figure 10. Final series verification for forecasts from v (red) and v (blue) against temperature from surface-based stations over North America as a function of lead times (h) for forecasts initialized at 00 UTC (12 UTC) in left (right) panel for the summer period. The standard deviations (biases; forecast minus observation) are shown in the top (bottom) panel and were obtained from an average over 59 cases. For each comparison, the difference between the two experiments (red minus blue) is shown in the bottom part of the figure where the grey shaded area represents a 90% confidence interval. 21

22 K K 1.5m T d Summer 00 UTC 12 UTC a) Std Dev b) Std Dev c) Bias d) Bias Lead Time (hours) Lead Time (hours) Figure 11. As in Fig. 10, but for dew point temperature. 22

23 K K 1.5m T Winter 00 UTC 12 UTC a) Std Dev b) Std Dev c) Bias d) Bias Lead Time (hours) Lead Time (hours) Figure 12. As in Fig. 10, but for the winter period. 23

24 K K 1.5m T d Winter 00 UTC 12 UTC a) Std Dev b) Std Dev c) Bias d) Bias Lead Time (hours) Lead Time (hours) Figure 13. As in Fig. 10, but for dew point temperature and the winter period. 24

25 ETS ETS ETS ETS 00 UTC 12 UTC a) +00 to +12h b) +00 to +12h c) +12 to +24h d) +12 to +24h e) +24 to +36h f) +24 to +36h g) +36 to +48h h) +36 to +48h Figure 14. Final series verification for forecasts from v (red) and v (blue) against 12-h precipitation accumulation measured in terms of the equitable threat score (ETS) as a function of accumulation thresholds (greater or equal to; mm) for forecasts initialized at 00 UTC (12 UTC) in left (right) panel for the summer period. Verifications for accumulation from 0h to +12h are shown in top row; +12h to +24h in second row; +24h to +36h in third row, and +36h to +48 in bottom row. The scores were obtained from an average over 59 cases. For each comparison, the difference between the two experiments (red minus blue) is shown in the bottom part of the figure where the grey shade represents a 90% confidence interval. 25

26 ETS ETS ETS ETS 00 UTC 12 UTC a) +00 to +12h b) +00 to +12h c) +12 to +24h d) +12 to +24h e) +24 to +36h f) +24 to +36h g) +36 to +48h h) +36 to +48h Figure 15. As in Fig. 14, but for the winter period. 26

27 6. Evaluation of the parallel run The parallel run of the RDPS began on 15 July 2014 (although the complete set of sub-systems, including UMOS and SCRIBE, only started on 24 July 2014) and lasted until November 2014 (more than 3 months) Objective evaluation The objective verification was computed using 118 cases from the parallel run (as in the final series) starting on 15 July UTC and ending on 11 September UTC. Figure 16 and Figure 17 show the comparison with radiosonde observations for 24h and 48h forecasts, respectively. The improvements are similar to the results from the final series (see Figures 5 and 6), except for the degradation of the geopotential height bias above 500 hpa, especially in the 24h forecasts (note that the same apparent degradation was also observed in the GDPS v.4.0.0; see GDPS-TN). We remark that temperature measurements from radiosondes (and thus geopotential height) can be significantly biased (see e.g. Sun et al. 2013) and that no bias correction is currently applied in our systems. Therefore, it is difficult to draw any conclusion based on radiosonde observations alone. On the other hand, as shown in the GDPS-TN, a comparison of the GDPS forecasts with ECMWF analyses (in which radiosonde temperatures are bias corrected) did not revealed any degradation in geopotential or temperature biases. Figure 18 show the verification of precipitable water forecasts against the values derived from GB- GPS ZTD observations. The improvements are similar to the results seen in the final series (see Figure 7). Figure 19 and Figure 20 show the verification for screen-level temperature and dew point, respectively. Improvements can be seen, but not as much as in the final series (see Figures 8 and 9). Figure 21 shows the precipitation accumulation verification in terms of ETS. Some improvements can be seen for some thresholds at most lead times. 27

28 Figure 16. As in Fig. 3, but for 118 cases of the parallel run in summer

29 Figure 17. As in Fig. 3, but for 118 cases of the parallel run in summer 2014 and for 48 h forecasts. 29

30 Figure 18. As in Fig. 9, but for 118 cases of the parallel run in summer

31 K K 1.5m T Summer 00 UTC 12 UTC a) Std Dev b) Std Dev c) Bias d) Bias Lead Time (hours) Lead Time (hours) Figure 19. As in Fig. 10. but for 118 cases of the parallel run in summer

32 K K 1.5m T d Summer 00 UTC 12 UTC a) Std Dev b) Std Dev c) Bias d) Bias Lead Time (hours) Lead Time (hours) Figure 20. As in Fig. 11. but for 118 cases of the parallel run in summer

33 ETS ETS ETS ETS a) +00 to +12h 00 UTC 12 UTC b) +00 to +12h c) +12 to +24h d) +12 to +24h e) +24 to +36h f) +24 to +36h g) +36 to +48h h) +36 to +48h Figure 21. As in Fig. 14. but for 118 cases of the parallel run in summer

34 6.2. Subjective evaluation The output of the RDPS parallel run was evaluated by CMC operational meteorologists for most of the summer and part of the autumn of Daily evaluations were performed by verifying forecasts from RDPS v and RDPS v against the operational analysis at 00 and 12 UTC. Forecasts with lead times of 12, 24, 36, and 48-hours were verified for mass fields (geopotential height at 500 hpa and sea-level pressure) and for quantitative precipitation forecasts (QPF). In the shorter lead-times, results showed small differences when it came to the verification of the geopotential height at 500 hpa and sea-level pressure. The differences became more frequent at 36 hours and more significant at 48 hours in favour of RDPS v At 48-hour lead time, the two systems were judged to be equivalent nearly two-thirds of the time, either having the correct solution or not. The remaining third of the time, the new RDPS was preferred over the operational model by a small margin. When considering the verification by region, a slight gain was noted for v over the Pacific and slightly more over central North America. It is over the Atlantic regions where we saw a more significant gain with RDPS v Where the operational RDPS and new RDPS were different, two thirds of the cases were in favour of the v solution. The timing and intensity of systems were better handled over the Atlantic regions, which had a positive impact for QPF as well. The subjective evaluation also showed slight gains for v over the Arctic, which were more pronounced at 500 hpa. Results for the QPF showed a more pronounced signal in favour of RDPS v over the Atlantic region, whereas they were judged to be equivalent over central regions, and somewhat better over the Pacific region. In summary, the subjective assessment concluded that the two systems are essentially equivalent in the first 24 hours, but RDPS v performs better on day 2, especially in terms of QPF and mass fields over regions of eastern Canada. 7. Performance of dependent systems 7.1. UMOS Training of the UMOS system for RDPS v was carried out using the hindcasts (final series of forecasts) for the periods from February 1 st to March 31 st, 2011 for the winter season and from July 1 st to August 30 th, 2011 for the summer season. In addition, training for the summer season was advanced using data from the parallel run. Verifications performed in both the summer (July 24 th to September 30 th, 2014) and winter (March 3 rd to March 31 st, 2011) seasons show that the performances of the UMOS-RDPS system are globally maintained although some individual stations show greater or weaker performances. Such variations are normal, and moreover, individual performances should generally improve with the addition of more cases in the UMOS statistical database. 34

35 7.2. SCRIBE Since the forecasts for days 1 and 2 in the SCRIBE system originate from the RDPS, the results from the RDPS v parallel run were verified using the SCRIBE verification system for the period from August 1 st to September 30, A statistical post-processing (UMOS) is performed for cloudiness, temperature at 2 m, probability of precipitation, and the direction and speed of the wind. Other weather elements are generated directly from model output. Observing stations used by the SCRIBE verification system. The following weather elements were verified for the 00, 06, 12, and 18Z model runs, for the 136 stations shown in the figure above, and for the 0-48 hour forecast period: Precipitation accumulation Cloudiness Probability of precipitation or PoP Maximum (daytime) and minimum (nighttime) temperatures Precipitation type Wind speed Wind direction The verification results generally indicated a neutral outcome. However, there was a slight improvement for the RDPS v forecast accumulation for the 0 mm category (no precipitation), as well as for the probability of precipitation, especially for the category 50%. Improvements were 35

36 also noticed in the prediction of wind speeds from 0 to 19 km/h and, to a lesser extent, from 20 to 39 km/h. For the predicted precipitation types and wind direction, the results showed a slight degradation compared to RDPS v However, these differences are not considered to be significant Other The impact of changes to the RDPS (from v to v.4.0.0) on the various sub-systems which depend on RDPS output to drive their forecasts and/or analyses was in most cases largely neutral. This was true for the High-Resolution Deterministic Prediction System (HRDPS) West window, the Regional Deterministic Wave Prediction System (RDWPS), the Integrated Nowcasting System (INCS), the Regional Air Quality Deterministic Prediction System (RAQDPS), and the Regional Deterministic Air Quality Analysis (RDAQA). For three other systems, in particular, 1) the Regional Deterministic Prediction Analysis (RDPA), otherwise known as the Canadian Precipitation Analysis (CaPA), 2) the Regional Deterministic Prediction System Coupled to the Gulf of St. Lawrence (RDPS-CGSL), and 3) the Regional Marine Prediction System for the Gulf of St. Lawrence (RMPS-GSL), major upgrades were tested in parallel runs over the summer of Based on the results of these runs, new versions of these systems were approved for implementation into operations at the same time as the implementation of RDPS v For further information concerning the changes made to each of these systems, as well as the performance of the parallel runs, the reader is referred to the appropriate technical note. The list of changes to operational systems at the CMC is available on the following web page: 8. Availability of products With this update, there are no changes to the timing or offering of products. 9. Summary of the results The updated version of the RDPS provides improved forecast accuracy relative to the previous system, version 3.2.1, based on results from two sets of two-month R&D data assimilation and forecast experiments during 2011 and also the parallel run during the summer and fall of The improvements were shown to result from the combined impact of numerous changes, notably: the replacement of 4DVar by 4DEnVar, an improved treatment of radiosonde and aircraft observations, an improved radiance bias correction procedure, and the assimilation of new radiance and GB-GPS observations. Due to the replacement of 4DVar with 4DEnVar, the new system is more computationally efficient and easier to parallelize, facilitating a doubling of the analysis increment horizontal resolution, which would have been prohibitively expensive with the 4DVar-based system. 36

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