Figure 1
Illustration of microphysical processes in the two-ice scheme. Aco is autoconversion, col collection, con condensation, dep deposition, eva evaporation, fre freezing, mel melting, sed sedimentation, sub sublimation, and WBF is the WBF process.
Figure 2
Same as Figure 1 but for the three-ice scheme.
Table 1
The proposed set of parameters for graupel. The diameter of a graupel particle is denoted by D and its density by ρg.
| PARAMETER | PROPOSED VALUE |
|---|---|
| fall speed relation | |
| density of graupel | ρg = 400 kg · m–3 |
| size distribution | Ng(D) = N0,ge–λD |
| intercept parameter | N0,g = 4 · 106 m–4 |
| mass-size relation | |
| collection efficiency | |
| autoconversion coefficient | kg = 10–3 s–1 |
| first WBF coefficient | |
| second WBF coefficient |
Table 2
Differences between the microphysical choices made in EMP1 and EMP2.
| PROCESS | EMP1 | EMP2 |
|---|---|---|
| evaporation parameterization | Kessler | Lopez |
| kr (Equation A1) | 5∙10–4 | 8∙10–4 |
| (Equation A1) | 3∙10–4 | 4∙10–4 |
| ks (Equation A3) | 2∙10–3 | 10–3 |
| cpart (Equation A13) | 1 | 0.75 |
Table 3
List of parameters used in the evaporation parameterization of Equation (3). Coefficients for the new graupel parameterization are listed.
| COEFFICIENT | RAIN | SNOW | GRAUPEL |
|---|---|---|---|
| C1 | 2.2295 | 1227 | 230 |
| C2 | –1/9 | 2/3 | 0.5 |
| C3 | 8.738 | 2373.7 | 149 |
| C4 | 0.3807 | 1 | 0.7075 |
Figure 3
Dependency of the evaporation rates on the mass fraction of rain (orange), snow (blue), and graupel (green) at p = 100000 Pa, T = 273.15 K, and RH = 90% using the Lopez evaporation parameterization.
Figure 4
Dependency of the evaporation rates of rain on its mass fraction at p = 100000 Pa, T = 273.15 K, and RH = 90%. The evaporation rates of rain from the Lopez evaporation parameterization (Equation (3)) are in solid orange. Rain evaporation rates with the reduction of evaporation following Equation (4) are in dashed orange, coefficient cr = 7∙10–7 s–1. For comparison, the original Kessler scheme for rain is also shown (blue).
Figure 5
The model domain (blue quadrangle) and the verification domain (orange quadrangle).
Figure 6
Statistical significance of the difference in bias, RMSE and STDE at the surface. Model configuration EMP1 is compared to EMP0 for the period from 2022-02-16 to 2022-05-10. Negative values of STDE and RMSE (in blue) mean improvement of EMP1 over EMP0, positive (in red) the opposite.
Figure 7
Asymptotic FSS for the period from 2022-02-16 to 2022-05-10, EMP0 in blue, EMP1 in orange. The mean of 6-hour precipitation accumulations over all runs and lead times is shown. In gray, the fraction of cases that exceeded the threshold is shown.
Figure 8
Precipitation frequency bias for the period from 2022-02-16 to 2022-05-10, EMP0 in blue, EMP1 in orange. The mean of 6-hour precipitation accumulations over all runs and lead times is shown.
Figure 9
Comparison of global radiation for 19 Czech stations between EMP1 (orange) and EMP0 (blue) for the spring period between 2022-02-16 and 2022-05-10.
Figure 10
Average of all snapshots of maximum radar reflectivity at each model level at the top of every hour of the 24-hour forecast on 2022-06-24. Orange: EMP1, blue: EMP0.
Figure 11
The 24-hour evolution of the difference in the vertical profiles of temperature (left panel) and qv (right panel) on 2022-06-24. Configuration with the 3-ice scheme is compared to the 2-ice scheme configuration. The contribution of evaporation is also plotted for qv.
Figure 12
The difference in the 72-hour precipitation accumulations (EMP1-EMP0) starting on 2022-02-17 at 00 UTC. Note the increased precipitation maxima over the Black Forest and Vosges mountain ranges with a subtle reduction of precipitation in the Rhine valley near Strasbourg, marked by the yellow circle. The grey isoline shows the model surface altitude of 500 m, the black one of 800 m above sea level.
Figure 13
The temperature budget difference between a run with Lopez evaporation without reduction of evaporation and EMP1. Note the different split of contributions between turbulence (light blue) and dynamics (red), which was found to be responsible for worsening of the STDE of wind at 10 m. The selected case is on 2022-06-24.
Figure 14
The difference in the accumulated turbulent flux of temperature over 24 hours. Configuration EMP2 is compared to the reference EMP1.
Figure 15
Asymptotic FSS for the 6-hour precipitation accumulations for EMP1 (blue) and EMP2 (orange) for the period from 2022-06-20 to 2022-07-10. In gray, the fraction of cases that exceeded the threshold is shown.
Figure 16
Precipitation frequency bias for the period from 2022-06-20 to 2022-07-10, EMP1 in blue, EMP2 in orange. The mean of 6-hour precipitation accumulations over all runs and lead times is shown.
Figure 17
24-hour precipitation accumulations forecasted by EMP1 (top) and EMP2 (middle) ending at 12 UTC on 2022-06-29 compared to MERGE (bottom). Both model runs were initialized at 00 UTC on 2022-06-28.
Figure 18
Six-hour precipitation accumulation bias for EMP1 (blue) and EMP2 (orange) for the period from 2022-11-08 to 2022-11-29.
Figure 19
Asymptotic FSS for the 6h precipitation accumulations for EMP1 (blue) and EMP2 (orange) for the period from 2022-11-08 to 2022-11-25. The mean of 6-hour precipitation accumulations over all runs and lead times is shown. In gray is the fraction of cases when the given threshold was exceeded.
Figure 20
Statistical significance of the difference in bias, RMSE and STDE at the surface. Model configuration EMP2 is compared to EMP1 for the period from 2022-11-08 to 2022-11-29. Negative values of STDE and RMSE (in blue) mean improvement of EMP1 over EMP0, positive (in red) the opposite.
Figure 21
Comparison of global radiation fluxes for 19 Czech stations between EMP2 (orange) and EMP1 (blue) for the autumn period between 2022-11-08 and 2022-11-29.
Figure 22
72-hour precipitation accumulations forecasted by EMP1 (top) and EMP2 (middle) ending at 00 UTC on 2022-11-27 compared to observations by MERGE (bottom). A precipitation shadow is distinct diagonally on the Czech side of the German-Czech border, which lies on the top of the main ridge of the Ore Mountains, as precipitation moved from northwest to southeast. Although rain, snow, and graupel contributed to the precipitation accumulation, rain was dominant except for the highest parts of the mountains, where the amounts of solid and liquid precipitation accumulations were equal.