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  • View in gallery

    A flow diagram of the LBF and UIC forecast.

  • View in gallery

    Averaged DTS over the (a1)–(c1) 75 WRF forecasts, (a2)–(c2) PSR case 1, and (a3)–(c3) PSR case 2 with the different methods [SN (light blue bars), SN+LBF (green bars), SN+UIC (blue bars), SN+UIC+LBF (red bars)] used, for the different precipitation rate categories. The abscissa and ordinate are for the forecast days and DTS, respectively.

  • View in gallery

    Averaged frequency bias over the (a1)–(c1) 75 WRF forecasts, (a2)–(c2) PSR case 1, and (a3)–(c3) PSR case 2 with the different methods [CTL (light blue bar), SN (blue bars), SN+LBF (green bars), SN+UIC (orange bars), SN+UIC+LBF (red bars)] used, for the different precipitation rate categories. The abscissa and ordinate are for the forecast days and frequency bias, respectively.

  • View in gallery

    (a1)–(a3) Averaged TS and (b1)–(b3) frequency bias (BS) over the 75 WRF forecasts with the different methods [SN (red lines), SN+LBF (blue lines), SN+UIC (green lines), SN+UIC+LBF (orange lines)] used, for the different precipitation rate categories [(a1),(b1) light rain (<10 mm day−1), (a2),(b2) moderate rain (10–24.9 mm day−1), and (a3),(b3) heavy rain (25–49.9 mm day−1)]. The color circles marked on the color lines denote that the relevant differences between the improvement methods and the CTL are significant at the confidence level of 95%.

  • View in gallery

    Total accumulative rainfall distribution (above 50 mm) for the PSR events for the observations (OBS1: case 1, OBS2: case 2), and in the forecast experiments at the different lead times [(a1)–(d1) 3 days, (a2)–(d2) 5 days, and (a3)–(d3) 7 days] and using the different experiment schemes [(a1)–(a3), (c1)–(c3) CTL and (b1)–(b3), (d1)–(d3) SN+UIC+LBF].

  • View in gallery

    Vertical profiles of the averaged IR for (a1)–(c1) H, (a2)–(c2) RH, and (a3)–(c3) T for (a1)–(a3) 1–5-day, (b1)–(b3) 6–10-day, and (c1)–(c3) 11–15-day forecasts during the experimental period, using the different methods. The color circles marked on the color lines denote that the relevant differences are significant at the confidence level of 95%.

  • View in gallery

    As in Fig. 6, but for the (a1)–(c1) U and (a2)–(c2) V.

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A Combined Approach to Improving the Regional Model Forecasts for the Rainy Season in China

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  • 1 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
  • | 2 Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, and State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China
  • | 3 College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, China
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Abstract

A combined forecasting methodology, into which the spectral nudging, lateral boundary filtering, and update initial conditions methods are incorporated, was employed in the regional Weather Research and Forecasting (WRF) Model. The intent was to investigate the potential for improving the prediction capability for the rainy season in China via using as many merits of the global model having better predictability as it does for the large-scale circulation and of the regional model as it does for the small-scale features. The combined methodology was found to be successful in improving the prediction of the regional atmospheric circulation and precipitation. It performed best for the larger magnitude precipitation, the relative humidity above 800 hPa, and wind fields below 300 hPa. Furthermore, the larger the magnitude and the longer the lead time, the more obvious is the improvement in terms of the accumulated rainfall of persistent severe rainfall events.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Donghai Wang, wangdh7@mail.sysu.edu.cn

Abstract

A combined forecasting methodology, into which the spectral nudging, lateral boundary filtering, and update initial conditions methods are incorporated, was employed in the regional Weather Research and Forecasting (WRF) Model. The intent was to investigate the potential for improving the prediction capability for the rainy season in China via using as many merits of the global model having better predictability as it does for the large-scale circulation and of the regional model as it does for the small-scale features. The combined methodology was found to be successful in improving the prediction of the regional atmospheric circulation and precipitation. It performed best for the larger magnitude precipitation, the relative humidity above 800 hPa, and wind fields below 300 hPa. Furthermore, the larger the magnitude and the longer the lead time, the more obvious is the improvement in terms of the accumulated rainfall of persistent severe rainfall events.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Donghai Wang, wangdh7@mail.sysu.edu.cn

1. Introduction

China is in the East Asian monsoon region. The onset of the East Asian summer monsoon is a key indicator characterizing the abrupt transition from the dry season to the rainy season and its subsequent seasonal procession (from April to August) (Ding and Chan 2005; Wu and Zhang 2011). Heavy rains cause mudslides and flooding in all the areas affected, and the persistent severe rainfall (PSR) causes severe impacts over larger areas and longer durations than “normal” torrential rain (Zhai et al. 2013), which are the distinguishing features of the rainy season, posing a considerable threat to human safety and economic stability. Improving the predictability of heavy rains and PSR during the rainy season in China is of great importance.

Incessant succession of theoretical and technological innovations has greatly contributed to the improvement of numerical weather prediction skills (Simmons 2011), with the weather forecast range achieving currently lead times of more than 7 days (http://www.emc.ncep.noaa.gov/gmb/STATS_vsdb). According to theoretical research on predictability (Lorenz 1965; Mu et al. 2006), the predictability period of daily forecasts can reach 2–3 weeks, and the large-scale circulation has better predictability than the smaller-scale disturbance (Lorenz 1969; Chen et al. 2010). However, since the discontinuity and nonlinearity of precipitation, the predictability of precipitation is lower than other continuity variables (Dong et al. 2015). Consequently, starting from the perspective of the better predictability of the large-scale circulation, an effort for extending the forecast lead time for precipitation, especially for heavy rains and PSR events, is worthwhile and reasonable to enhance disaster prevention and mitigation capabilities.

For the large-scale circulation, spectral nudging (SN) is a scale-selective interior constraint technique (von Storch et al. 2000) in a regional model. When the large-scale systems develop to deeper levels, the SN is confined to the higher altitudes, and the local convection at lower levels can develop freely. The application of the SN improves significantly the prediction of large-scale circulation (Miguez-Macho et al. 2004), Arctic temperature (Glisan et al. 2013), and precipitation (Miguez-Macho et al. 2005; Liu et al. 2012) forecasts in the Weather Research and Forecasting (WRF) Model. The SN was also applied to constrain the regional climate model (RCM) with the general circulation model (GCM) (Xu and Yang 2015), and the results indicated that the SN minimized climate drifts resulting from both the GCM and RCM biases. In addition, the SN has been applied in multiple regional climate models, such as western Europe (Feser 2006), North America (Kanamaru and Kanamitsu 2007; Spero et al. 2014), and East Asia (Cha and Lee 2009; Zhao et al. 2016), and its application improves significantly the prediction of regional atmospheric circulation and precipitation forecasts.

On the other hand, as the resolution of the model became higher and higher, the accuracy of the initial conditions (IC) and lateral boundary conditions (LBC) became increasingly important, especially for the mid- and long-range forecast (Li et al. 2013). Moreover, an accurate small-scale forecast can be produced by the regional model with the use of the higher-resolution and better parameterized schemes (Grazzini and Vitart 2015), and it may not be produced by the coarser global fields (Schwartz and Liu 2014). The blending technique is an effective method to retain the large-scale forecasts of the forcing fields and the small-scale features of the regional model (Yang 2005; Wang et al. 2014a) for IC. It has been widely used in the ensemble prediction within 3 days to generate IC perturbations in a regional ensemble prediction system, the results showed that blending ensures that the scale of the IC perturbations matches the scale resolved by regional model and has the best overall performance (Wang et al. 2014b; Zhang et al. 2015). The blending was also applied through assimilation in Typhoon WRF to improve the typhoon track forecasts (Hsiao et al. 2015). For LBC, the lateral boundary filtering (LBF) method is based on the low-pass filtering to maintain the large-scale circulation, which possesses better predictability than the smaller-scale one in the forcing fields. Our previous study used LBF to improve the forecasting of PSR events, and the results showed that the effect of the LBF scheme is more obvious at the lead times of 7–11 days (Zhao et al. 2016).

In the present study, the SN is used to constrain the interior of the regional model toward the large-scale driving data, and the LBF was used to improve the large-scale circulation for LBC, along with the “update cycle” method to retain the small-scale features for IC, by using multiscale blending (MSB) (Zhao et al. 2017a, b) for 15-day forecasts in WRF, so as to extend the forecast lead time of the regional atmospheric circulation and precipitation forecasts for the rainy season in China.

2. Data and methodology

The data used in this study are as follows: 1) the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) Final Operational Global Analysis (FNL) data (Kalnay et al. 1996) were used (horizontal resolution: 1° × 1°; temporal resolution: 6 h) to objectively validate the improvement methodology; 2) the Global Forecast System (GFS) data (horizontal resolution: 0.5° × 0.5°) were used to provide the regional model IC, LBC, and SN fields, with the initial forecast time being at 0000 UTC for 15-day forecasts during April–August 2016 and the temporal resolution of the first 10 days of GFS forecasting being 3 h but 12 h for the last 5 days; and 3) the precipitation dataset of basic daily meteorological variables was from Chinese national surface weather stations, covering April–September 2016.

The regional model used was the Advanced Research WRF Model (version 3.7.1), which was run with a 63-layer configuration with the model top being a constant pressure surface of 10 hPa in this study. Single-deck nesting was used with horizontal grid spacings of 12 km (560 × 420 grids). The model physics options include the WRF double-moment 6-class (WDM6) microphysics scheme (Lim and Hong 2010), the Kain–Fritsch cumulus convection parameterization scheme (Kain 2004), the Yonsei University (YSU) boundary layer scheme (Hong et al. 2006), the Rapid Radiative Transfer Model (RRTM) longwave radiation scheme (Mlawer et al. 1997), the Dudhia shortwave radiation scheme (Dudhia 1989), the Noah land surface scheme (Tewari et al. 2004), and the Monin–Obukhov surface layer scheme (Beljaars 1995).

The SN was used to constrain the interior of the regional model domain to the large-scale driving data as is done since version 3.1 of WRF. It was configured to nudge horizontal winds, potential temperature, and geopotential height with an interval of 6 h, starting from the initial time to the end of the forecast, while no nudging is conducted within the planetary boundary layer. The nudging fields were from the GFS prediction. The nudging coefficients for all variables were set to be 0.0003 s−1. The SN was applied to wavelengths longer than a threshold that is a function of domain size. The WRF domain size was about 6720 km × 5040 km in the zonal and meridional directions in this study, respectively; and the SN with wavenumber 4 in both the directions captures the driving field features of scale about 1680 km and 1260 km, respectively. Hence, the regional model error growth was caused mainly by the waves with their wavelengths greater than 2000 km (Vukicevic and Errico 1990), it was an appropriate choice in WRF runs, as done in Liu et al. (2012).

The LBF method (Zhao et al. 2016) began from the third-day forecasts (Fig. 1) and harmonic filtering was selected for spatial field scale separation in this study. The low-pass filtering was mainly performed for 0–6 waves, which was based on the dynamical features of the large-scale circulation for PSR events (Zhao et al. 2017c). The rest of the high-frequency waves were reserved by 50%, and the specific procedure was as follows:
e1
where Hall is the GFS forecasts, H0–6 is the low-pass wave band of 0−6 waves, and H is the high-frequency weakened field.
Fig. 1.
Fig. 1.

A flow diagram of the LBF and UIC forecast.

Citation: Monthly Weather Review 145, 11; 10.1175/MWR-D-17-0037.1

Based on the SN experiments, the update cycle for IC (UIC) was conducted for every 72-h forecast, with a 12-h running-in period for the model adaptation. As Fig. 1 showed, the SN was started from 0 to 3 days and did MBS at the 2.5-day mark, which was followed by 0.5-day running-in period, then a new SN was started from 3 to 6 days. So, the 15-day forecasts were conducted by five 3-day forecasts, and four UICs were done by using the MSB (Wang et al. 2014a; Zhao et al. 2017a, b). The MSB was composed of two steps. First, the large scales (0–4 waves) from GFS and WRF were filtered for all the SN variables and water vapor mixing ratio at 62 layers (except the first ground layer), with the two-dimensional discrete cosine transform filtering method incorporated (Denis et al. 2002). Second, the spectral blending was used to combine the large scales (0–4 waves) from GFS with the small scales (>4 waves) from WRF.

Including the control (CTL) experiments, there were five sets of experiments conducted in this study (Table 1): CTL, SN, SN+LBF, SN+UIC and SN+UIC+LBF, with 15-day runs done for a total of 75 cases in each experiment set. The initial forecast times for the 75 cases were the odd number days for the rainy season in China (0000 UTC 1 April–0000 UTC 29 August 2016). There are two PSR cases over the Yangtze River valley included during the experimental period [case 1: 0000 UTC 18 June–0000 UTC 22 June (Cao and Zhang, 2016); and case 2: 0000 UTC 30 June–0000 UTC 6 July (Quan and He 2016; Zhao et al. 2017a)].

Table 1.

The experiment design.

Table 1.

The root-mean-square error (RMSE) was applied for the verification of the geopotential height (H; gpm), temperature (T; °C), relative humidity (RH; %), zonal wind (U; m s−1), and meridional wind fields (V; m s−1) at the different altitudes. The threat score (TS) and frequency bias were used for 24-h precipitation verification for the different precipitation rate categories [light rain (<10 mm day−1), moderate rain (10–24.9 mm day−1), and heavy rain (25–49.9 mm day−1)], and their difference TSs (DTS) averaged over the model runs between the CTL and that by the improvement methods were also calculated, as follows:
e2
where and are TSs of the improvement methods and CTL experiments, respectively. There was no verification for the daily precipitation above 50 mm due to fewer sample data. The improvement rate (IR) of RMSE for the improvement methods (), compared to CTL (), was also calculated, as follows:
e3

A Student’s t test was used to assess the statistical significance of the RMSE difference between the improvements methods run and the CTL forecasts for H, T, RH, U, and V at the different altitudes, with the 95% confidence level as the significance delimiter. For the TS and frequency bias, the hypothesis test via resampling the mutually exclusive and collectively exhaustive events (hits, false alarms, and misses), was also used for the different precipitation categories (<10, 10–24.9, and 25–49.9 mm day−1) (Livezey and Chen 1983; Hamill 1999).

3. Results

The forecast outcomes were first compared by calculating the DTS for 24-h precipitation (Fig. 2). In terms of light rain, the averaged DTS over the experimental period showed mostly a positive value after the 5-day forecast (Fig. 2a1), with the SN+LBF achieving slightly better results than the others. The performance of the improvement methods, relative to the CTL, fluctuated for the PSR cases, with the SN+LBF achieving mostly a positive value for the 3–11-day forecasts. All the improvement methods showed significant improvement for moderate rains (Figs. 2b1–b3), and the improvement became increasingly obvious as the lead times increased within the first 7 days, with the SN and SN+UIC performing slightly better than the other two methods. The positive DTS was larger in the PSR cases outcomes (Figs. 2b2–b3) than that of the experimental period average (Fig. 2b1) for the 4–7-day forecast period, and the SN+LBF and SN+UIC+LBF were relatively superior. For heavy rains (Fig. 2c1), the positive DTS was mainly shown in the 3–9-day forecasts, with the significant improvement mainly reflected in the SN+UIC and SN+UIC+LBF outcomes, among which the SN+UIC+LBF was slightly better.

Fig. 2.
Fig. 2.

Averaged DTS over the (a1)–(c1) 75 WRF forecasts, (a2)–(c2) PSR case 1, and (a3)–(c3) PSR case 2 with the different methods [SN (light blue bars), SN+LBF (green bars), SN+UIC (blue bars), SN+UIC+LBF (red bars)] used, for the different precipitation rate categories. The abscissa and ordinate are for the forecast days and DTS, respectively.

Citation: Monthly Weather Review 145, 11; 10.1175/MWR-D-17-0037.1

The frequency bias indicates whether the forecast system has a tendency to underforecast (bias < 1) or overforecast (bias > 1) events. The averaged bias over the 75 WRF forecasts, PSR case 1, and PSR case 2 with the different methods were shown in Fig. 3. The WRF forecasts showed an overforecast for the light rains and an underforecast for the moderate and heavy rains over the experimental period. In terms of light rain (Figs. 3a1–a3), the SN+LBF achieved slightly better results than the others. For moderate rains (Figs. 3b1–b3), SN+UIC and SN+UIC+LBF performed slightly better than the other methods, with the bias being the largest for the PSR case 2. For heavy rains, the efficiency of the improvement methods became increasingly obvious after the 5-day forecasts, with the SN+UIC+LBF achieving slightly better results than the others in the experimental period average (Fig. 3c1).

Fig. 3.
Fig. 3.

Averaged frequency bias over the (a1)–(c1) 75 WRF forecasts, (a2)–(c2) PSR case 1, and (a3)–(c3) PSR case 2 with the different methods [CTL (light blue bar), SN (blue bars), SN+LBF (green bars), SN+UIC (orange bars), SN+UIC+LBF (red bars)] used, for the different precipitation rate categories. The abscissa and ordinate are for the forecast days and frequency bias, respectively.

Citation: Monthly Weather Review 145, 11; 10.1175/MWR-D-17-0037.1

The relevant differences between the improvement methods and the CTL were also calculated at the confidence level of 95% for averaged TS (Figs. 4a1–a3) and frequency bias (BS; Figs. 4b1–b3). In terms of light rain, the SN+LBF and SN achieved better results than the other two methods, and the SN+LBF was better than the SN for the 5–12-day forecasts (Figs. 4a1–b1), with the most of them significant at the confidence level of 95%. For moderate rains (Figs. 4a2–b2), the significant improvement of TS was reflected in all the improvement methods for 1–8-day forecasts (Fig. 4a2), the SN+LBF, SN, and SN+UIC also showed the significant improvement for 9–12-day forecasts, and the significant improvement of BS was reflected in the SN+UIC and SN+UIC+LBF for the 3–9-day forecasts. For heavy rains (Figs. 4a3–b3), the significant improvement was also mainly reflected in the SN+UIC and SN+UIC+LBF for the 3–12-day forecasts, and the SN+UIC +LBF achieved slightly better than the SN+UIC. Thus, the larger the magnitude, the more obvious is the improvement by the SN+UIC+LBF.

Fig. 4.
Fig. 4.

(a1)–(a3) Averaged TS and (b1)–(b3) frequency bias (BS) over the 75 WRF forecasts with the different methods [SN (red lines), SN+LBF (blue lines), SN+UIC (green lines), SN+UIC+LBF (orange lines)] used, for the different precipitation rate categories [(a1),(b1) light rain (<10 mm day−1), (a2),(b2) moderate rain (10–24.9 mm day−1), and (a3),(b3) heavy rain (25–49.9 mm day−1)]. The color circles marked on the color lines denote that the relevant differences between the improvement methods and the CTL are significant at the confidence level of 95%.

Citation: Monthly Weather Review 145, 11; 10.1175/MWR-D-17-0037.1

To understand the effect of the SN+UIC+LBF on the forecasting of total accumulative rainfall at different times, the results for the two PSR cases were further analyzed in detail (Fig. 5). For case 1, the precipitation intensity using the SN+UIC+LBF (Figs. 5b1–b3) was close to the observation (Fig. 5, OBS1), with the forecasted rainband of the accumulated rainfall above 50 mm showed a southward deflection by using the CTL (Figs. 5a1–a3), and the SN+UIC+LBF produced better forecasts with the increasing lead times. For case 2, the rainband’s range and the accumulated rainfall above 100 mm were closer to the observation (Fig. 5, OBS2) by using the SN+UIC+LBF, especially for the lead time of 3 (Fig. 5d1) and 7 (Fig. 5d3) days, and the SN+UIC also showed better performance (figures not presented here) (see also Zhao et al. 2017a). In general, the better performance of the SN+UIC+LBF is owing to the inclusion of the UIC scheme, which combined the respective advantages of the GFS and WRF forecasts to improve the ICs.

Fig. 5.
Fig. 5.

Total accumulative rainfall distribution (above 50 mm) for the PSR events for the observations (OBS1: case 1, OBS2: case 2), and in the forecast experiments at the different lead times [(a1)–(d1) 3 days, (a2)–(d2) 5 days, and (a3)–(d3) 7 days] and using the different experiment schemes [(a1)–(a3), (c1)–(c3) CTL and (b1)–(b3), (d1)–(d3) SN+UIC+LBF].

Citation: Monthly Weather Review 145, 11; 10.1175/MWR-D-17-0037.1

The averaged IR profiles of the RMSE for the different forecast times, during the experimental period, were shown in Figs. 6 and 7. For the geopotential height (H) field (Figs. 6a1–c1), the overall outcomes of the improvement methods decreased the RMSE, compared to the CTL forecasts. The SN and SN+LBF produced better results than the other two methods, and the SN+LBF was better than the SN for the 6–15-day forecasts (Figs. 6b1–c1), especially for the altitudes above 600 hPa, with most of them significant at the confidence level of 95%. There was an obvious difference of IR between the relative humidity (RH) (Figs. 6a2–c2) and the H fields, and the averaged IR of the SN+UIC+LBF was the largest, followed by the SN+UIC above 800 hPa, with the SN and SN+LBF having similar and relatively larger IR below 800 hPa. The differences between the improvement methods and CTL were statistically significant below 600 hPa where a large amount of water vapor concentrated. The improvements of the SN+UIC+LBF and SN+UIC were relatively robust throughout all the altitudes. The IRs of the SN+LBF outcomes were negative values from 300 to 200 hPa for the 6–15-day forecast period. For the temperature field T, all the RMSEs decreased by using the improvement methods, compared to the CTL outcomes (Figs. 6a3–c3). The SN and SN+LBF had similar performance, with relatively larger IR for the various forecast periods, especially for those below 600 hPa. The significant improvement was also mainly reflected in the altitudes below 600 hPa, with a wider vertical range (almost all the altitudes) for the 1–5-day forecast period.

Fig. 6.
Fig. 6.

Vertical profiles of the averaged IR for (a1)–(c1) H, (a2)–(c2) RH, and (a3)–(c3) T for (a1)–(a3) 1–5-day, (b1)–(b3) 6–10-day, and (c1)–(c3) 11–15-day forecasts during the experimental period, using the different methods. The color circles marked on the color lines denote that the relevant differences are significant at the confidence level of 95%.

Citation: Monthly Weather Review 145, 11; 10.1175/MWR-D-17-0037.1

Fig. 7.
Fig. 7.

As in Fig. 6, but for the (a1)–(c1) U and (a2)–(c2) V.

Citation: Monthly Weather Review 145, 11; 10.1175/MWR-D-17-0037.1

The zonal wind (U; Figs. 7a1–c1) and meridional wind (V; Figs. 7a2–c2) fields had similar verification scores, yielding the same performance as that for H and T above, with the positive IR achieved for all the improvement methods. There was a slight difference among the four methods for the 1–5-day forecast period (Figs. 7a1–a2), and the significant improvement was reflected in all the levels for U, and only ones above 500 hPa for V. For the 6–10- and 11–15-day forecast periods, the SN and SN+UIC+LBF were slightly better than the others for the wind fields (U; V), and the SN+UIC+LBF was significantly better for the levels below 300 hPa, especially for V. Also, the altitude range of the significant improvement for U was wider than that for V, and the SN+UIC+LBF performed best.

4. Conclusions and discussion

To extend the forecast range of the regional atmospheric circulation and precipitation for the rainy season in China, using the WRF Model, a set of improvement methods and their performance were investigated in this study. The results of the five experiments using the schemes of SN, LBF, and UIC were analyzed and verified, in terms of precipitation and the related meteorological variables.

For precipitation, the efficiency of the improvement methods was mainly reflected in the 3–9-day forecasts, with more significant improvement for moderate rains. The advantages of the different methods were closely dependent on the precipitation magnitude (i.e., in general, the larger the magnitude, the more significant was the improvement), especially by using the SN+UIC+LBF. The SN+UIC+LBF improved the prediction of the rainband’s range and accumulated rainfall above 50 mm for the two PSR cases at the lead times of 3–7 days, and, the longer the lead time, the more obvious was the improvement by the SN+UIC+LBF.

The overall outcome of the improvement methods decreased the RMSE, compared to the CTL forecasts, for the geopotential height, relative humidity, temperature, and wind fields at all the altitudes. The difference among the improvement methods became increasingly obvious with the increasing forecast days (lead times). The SN+LBF produced better results than the others for the levels above 600 hPa for the geopotential height and the levels below 600 hPa for temperature. For the relative humidity, the SN+UIC and SN+UIC+LBF produced better than the others, since they combined the advantages of the GFS and WRF forecasts to improve the ICs. The improvements for the relative humidity may have a greater contribution to the better performance of SN+UIC and SN+UIC+LBF in the precipitation forecasts. The improvements of the SN+UIC+LBF were the largest for the relative humidity and wind fields for the levels above 800 hPa and below 300 hPa, respectively.

These results indicated that the SN, LBF, and UIC methods can significantly improve the regional atmospheric circulation and precipitation forecasts. The improvement of the SN method in precipitation forecasting is mainly attributable to the better simulation of the large-scale circulation and water vapor flux convergence (Zhao et al. 2016). The LBF method can improve the large-scale forecasts, including the geopotential height and temperature. And, the UIC method can improve the relative humidity forecasts, and in turn improve the precipitation forecasts.

Although the features and advantages of the various methods were different from each other, the integrated method (SN+UIC+LBF) performed distinctly best as a whole, especially for the larger magnitude precipitation and the longer forecast lead time, so the combined forecasting methodology could be applied to enhance disaster prevention and mitigation capabilities. A forthcoming study will be optimizing the best set of parameterization configurations; analyzing the dynamics, thermodynamics, and many other aspects of the precipitation systems for different precipitation rate categories; and will have a more in-depth investigation into the function of the methods, as well as designing a new skill score exploring for better quantitative verification and analysis.

Acknowledgments

This study was jointly supported by the National Natural Science Foundation of China (Grants 41775097 and 91437221), the National Key Basic Research Program of China (Grant 2012CB417204), China Special Fund for Meteorological Research in the Public Interest (Grant GYHY201506002), and the China Scholarship Council.

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