Development and evaluation of spectral nudging strategy for the simulation of summer precipitation over the Tibetan Plateau

. Precipitation is the key component determining the water budget and climate change of the 15 Tibetan Plateau (TP) under a warming climate. This high-latitude region is regarded as “the Third Pole” of the Earth and the “Asian Water Tower” and influences the eco-economy of downstream regions. However, the intensity and diurnal cycle of precipitation are inadequately depicted by current reanalysis products and regional climate models (RCMs). Spectral nudging is an effective dynamical downscaling method used to improve precipitation simulations of RCMs by preventing simulated fields from drifting 20 away from large-scale reference fields, but the most effective manner of applying spectral nudging over the TP is unclear. In this paper, the effects of spectral nudging parameters (e.g., nudging variables, strengths and levels) on summer precipitation simulations and associated meteorological variables were evaluated over the TP. The results show that using a conventional continuous integration method with a single initialization is likely to result in the overforecasting of precipitation events and the 25 overforecasting of horizontal wind speeds over the TP. In particular, model simulations show clear improvements in their representations of downscaled precipitation intensity and its diurnal variations, atmospheric temperature and water vapor when spectral nudging is applied towards the horizontal wind and geopotential height rather than towards the potential temperature and water vapor mixing ratio. This altering to the spectral nudging method not only reduces the wet bias of water vapor in the lower 30 troposphere of the ERA-Interim reanalysis ( when it is used as the reference fields) but also alleviates the cold bias of atmospheric temperatures in the upper troposphere, while maintaining the accuracy of horizontal wind features for the simulated fields. The conclusions of this study imply how reference fields errors impact model simulations, and these results may improve the reliability of RCM results used to study the long-term regional climate change. the CMORPH precipitation data indicate that conventional continuous simulation cannot reproduce actual precipitation patterns and largely overestimate precipitation events over the western and northern TP. The use of spectral nudging in the WRF model reduces such 375 overestimations but does not always overcome all deficiencies when simulating the precipitation intensity and its diurnal cycle. Spectral nudging experiments with decreased nudging coefficients (4.5 × 10 -5 s -1 ) for wind and potential temperature showed comparable results with conventional continuous simulation in terms of precipitation, associated water vapor transport and temperature simulations. In addition, allowing spectral nudging towards water vapor mixing ratio in more 380 atmospheric layers within the troposphere can reduce the wet bias of water vapor. This improvement cannot compensate for the artificially introduced extra wet bias when water vapor in the model field is relaxed towards the ERA-Interim reanalysis. Therefore, although the ERA-Interim reanalysis has been


Introduction
The Tibetan Plateau (TP) is referred to the Third Pole of the Earth and its average elevation is greater than 4,000 m. The TP has a complex topography and is also represented as the "Asian Water Tower". Its powerful thermodynamic and dynamic effects not only significantly influence regional climate patterns 40 and climate change but also have great impacts on the processes of the Asian monsoon system and westerlies (Immerzeel and Bierkens, 2010;Wu et al., 2007). Regional variations of atmospheric heating resources over the TP significantly regulate the summer Asian monsoon and associated precipitation (Zhao and Chen, 2001). Precipitation is an essential meteorological variable to reveal the water cycle and surface energy balance (Palmer and Raisanen, 2002). It is thus imperative to realistically simulate 45 variations of precipitation in both spatial and temporal distribution over the TP, which is also a vital component that can assess the performance of models (Bohner and Lehmkuhl, 2005;Karmacharya et al., 2017).
The major limitations for climate studies over the TP are attributed to the sparse observation datasets in both spatial and temporal coverage. Consequently, atmospheric reanalysis products or global climate 50 model (GCM) output are often used, but they are still too coarse to represent the complex terrain of the TP (Lang and Barros, 2004;Palazzi et al., 2013). In addition, the seasonal-to-interannual prediction of precipitation, especially with respect to extreme precipitation events over the southern and southeastern TP, remains poorly modeled by the GCM (Su et al., 2013;Wang et al., 2016). Regional climate models (RCMs) with higher resolution is thus used to dynamically downscale the coarse output of coupled GCMs 55 or reanalysis datasets (Glisan et al., 2013;Lo et al., 2008;von Storch et al., 2000). RCMs have been widely applied to study monsoon variations, regional climate change and perform well in precipitation predictions (Gao et al., 2015;Jiang et al., 2019). Compared to global reanalysis, RCMs generally produce better annual variation and long-term trends of precipitation over the TP during wet seasons (Gao et al., 2015;Jiang et al., 2016). 60 However, these advantages of RCMs are limited to the time range of simulations. When forecast time is beyond 36 h, models' skill is gradually diminishing (White et al., 1999). This is mainly affected by the continuously accumulated deviations between the RCM's circulation fields and the large-scale driving fields as time progresses (Miguez-Macho et al., 2004;Waldron et al., 1996). As a solution, the nudging method was proposed and applied for RCMs to ensure the simulated field be consistent with the large-65 scale driving fields while eliminating the spurious reflections of large-scale circulations inside the domain (Miguez-Macho et al., 2004;von Storch et al., 2000;Miguez-Macho et al., 2005).
Two forms of nudging technique, including grid nudging and spectral nudging, could be used in the Weather Research and Forecasting (WRF) model. Grid nudging in WRF adds one or more artificial tendency terms to relax the model state toward the observed fields at each grid point, which is based on 70 the differences between the model solution and the observations (Stauffer and Seaman, 1994). The horizontal wind, potential temperature and water vapor mixing ratio can be nudged in grid nudging, and it has been successfully used for regional climate simulations (Bowden et al., 2013;Bowden et al., 2012;Lo et al., 2008). However, grid nudging is spectrally indiscriminate, because it modulates the model solution with the same strength, which neglects the features of the variations of interpolated small-scale 75 for the model fields.
Meanwhile, spectral nudging is scale-discriminated, thus only the wavelength longer than the selected wavenumbers of model fields will be nudged towards the driving fields. Spectral nudging is capable to alleviate the bias while keeping the inner variability for model domain. Besides horizontal wind and potential temperature can be nudged in spectral nudging, spectral nudging is also applied to geopotential. 80 Compared with grid nudging, spectral nudging reduces suspicious reflections at the lateral boundaries and removes the influence of domain size and position of the RCM (Miguez-Macho et al., 2004). These advantages of spectral nudging not only improve the variability of mean or extreme precipitation simulations , but also efficiently improve large-scale atmospheric circulation simulations . By the efforts of Sepro et al. (2014), spectral nudging can be applied 85 toward water vapor mixing ratio and restrict nudging toward potential temperature and water vapor mixing ratio above the tropopause. These modifications to fundamental spectral nudging strategy improve the 2-m temperature, upper-layer cloud cover and precipitation simulation over the North America and the contiguous United States (CONUS). Although various sensitivity experiments have been devoted to examine the effect of spectral nudging on regional climate simulations in RCMs (Tang 90 et al., 2018;Moon et al., 2018), studies on how to identify a most effective nudging strategy for precipitation simulation over the TP are limited. Because optimal nudging strategies of different regions may not be suitable for the TP with sophisticated and high topography. The effects of spectral nudging toward water vapor mixing ratio on precipitation simulation are also needed to be evaluated.
In this study, different spectral nudging strategies were conducted over the TP for the July of 2008. The 95 influences of nudging parameters, including nudging variables, strength and model levels where nudging is applied, on precipitation simulations were explored, with a particular focus on the performance of extreme precipitation events. In the following sections, the WRF model, experimental design and validated data are described in section 2. Section 3 shows the validation of the various WRF simulations against observation data, and analysis of the effects of spectral nudging on large-scale atmospheric 100 circulation are discussed in section 4. In the section 5, the conclusions of the research are presented.

WRF model configuration
The WRF model version 4.0 was used in this study. The capability of spectral nudging towards water vapor mixing ratio was added in this version. A two-nested domain used in this study is displayed in 105 Figure 1. The outer domain with 30-km spatial resolution provides the information from the large-scale processes. The inner domain with 10-km spatial resolution is the main study area and covers the complex terrain of the TP. The terrain of the region varies intensely in a short spatial range, which can exert strong turbulent drag on low-level atmospheric circulation and thus influence water vapor transport. To avoid the influence of regional difference when calculating the mean precipitation over the entire TP, 110 evaluations of precipitation simulations were also conducted on extreme (the highest 5 percentile value of) precipitation events.

Experimental settings
The physical options used in this study are followed to the settings of the High Asia Refined (HAR) data (Maussion et al., 2011;Maussion et al., 2014). Specifically, the Thompson scheme (Thompson et al., 115 2004), the Grell 3D ensemble scheme (Kain, 2004), the Dudhia shortwave radiation scheme (Dudhia, 1989) and Rapid Radiative Transfer model longwave radiation scheme (Mlawer et al., 1997), and the unified Noah Land Surface Model (Chen and Dudhia, 2001) were selected, with the exception that the Yonsei University scheme (Hong et al., 2006).
The initial and lateral boundary conditions were driven by the ERA-Interim reanalysis data with 6-h 120 temporal and 79 km spatial resolution (Dee et al., 2011). The sea surface temperatures were also derived from ERA-Interim. All seven simulations, including one without nudging and six with spectral nudging using various nudging strategies (Table 1) In spectral nudging simulations, cut-off wave number was set to a wavelength of 1000 km, for which has been validated to be the most appropriate nudging wavelength to simulate precipitation events in many regions (Gomez and Miguez-Macho, 2017;Yang et al., 2019). Therefore, the wavenumbers of X and Y directions were 5 and 4, respectively for the outer domain and 3 and 2, respectively for the inner domain.
Each nudging simulation was applied above the planetary boundary layer (PBL) so that allowing the 130 near-surface small-scale processes be freedom to respond to local processes.
The conventional continuous integration without nudging is designated "Control". Default spectral nudging simulation is designated "SN". The nudging coefficient is related to the nudging relaxation time, which indicates a predetermined time-scale how often nudging variables are relaxed toward the largescale driving fields. In SN simulation, nudging coefficients for horizontal wind, potential temperature, 135 and geopotential on both domains are the default value of 3.0×10 -4 s -1 (relaxation time scale of 50 min).
For water vapor mixing ratio, default coefficient is 0.1× 10 -4 s -1 (relaxation time scale of 24 h). An infinitely shorter relaxation time does not result a higher consistence between the simulated small-scale fields with the forcing fields (Alexandru et al., 2009;Omrani et al., 2012). The relaxation time of nudging variables is recommended to be equivalent to the temporal interval of the input driving data (Omrani et 140 al., 2013;Spero et al., 2018). Therefore, a nudging coefficient of 0.45×10 -4 s -1 (relaxation time scale of 6 h) may be appropriate. Following this perspective, the subsequent sensitivity experiments were conducted with a weaker nudging coefficient (longer relaxation time) for wind and temperature on basis of SN, such as SNlowU and SNlowT simulations. Sensitivity simulation was not applied to geopotential, because it has been shown a neglectable influence when spectral nudging toward geopotential with 145 different nudging coefficients (Spero et al., 2018). In WRF version 4, a new option "ktrop" was added to allow spectral nudging be applied toward water vapor mixing ratio. This option adds a lid for both potential temperature and water vapor mixing ratio fields at a predefined layer (nominally selected to represent the tropopause) above the PBL, while horizontal wind component and geopotential are not affected by the option. 150 As suggested from a 35-years analysis by using ERA-Interim reanalysis data, the pressure level of tropopause over the TP varies between 93 and 106 hPa, with a mean value of 100 hPa during summer https://doi.org/10.5194/gmd-2020-394 Preprint. Discussion started: 29 December 2020 c Author(s) 2020. CC BY 4.0 License. (Zhou et al., 2019). In this study, the associated level of tropopause was 39 (namely, ktrop = 39). In this paper, it is important to examine the influence of adding the lid at different model layer, since the tropopause layer over the TP is much higher than other regions. 155

Validation data for model evaluation and comparison
Model simulated precipitation were evaluated against the merged Climate Prediction Center (CPC) MORPHing technique (CMORPH) precipitation dataset (hereinafter called CMORPH) with a temporal and spatial resolution of 1 h and 0.1°, which is developed by the China Meteorological Administration (CMA). The original CMORPH data with a spatial resolution of 8 km and temporal resolution of 30 min 160 are firstly resampled to the 0.1° and 1 h spatial and temporal resolution. Then its systematic biases are corrected by 2400 rain gauges in China by using probability density function matching method. The corrected CMORPH is subsequently combined with hourly rain gauge analysis from automatic stations to derive the merged hourly precipitation dataset by applying the optimal interpolation methods (Joyce Xie et al., 2017). Many studies demonstrate the high consistency and an acceptable bias of 165 the CMORPH compared with the observed precipitation (Ou et al., 2020;Wei et al., 2018).
Beside the evaluation of precipitation, assessments of wind fields are also particularly important. The large-scale atmospheric circulation directly controls the atmospheric water vapor (AWV) transport between the TP and its surrounding area and exerts a great influence in the formation of precipitation.
Impacts of different spectral nudging strategies on the 500-hPa AWV transport were compared with the 170 fifth-generation global reanalysis of ECMWF, ERA5, which has a 31-km spatial resolution and 1-h temporal resolution. The horizontal wind fields of ERA5 over the TP have been verified with observations (He et al., 2019) and show the smallest wind bias compared to ERA-Interim reanalysis and an ensemble data assimilation system. In this study, ERA5 was interpolated to the 10-km downscaled grid resolution as the same to model output. 175

Impacts of nudging strategy on mean precipitation simulations
The spatial consistency between simulated results and CMORPH was investigated by the monthly mean (larger than 0.1 mm/day) and extreme (the highest 5 percentile value of) precipitation (P95, mm/day; equals to 5.73 mm/day in this study). The monthly mean spatial distributions of the CMORPH 180 precipitation fields of July and its difference with WRF simulations over the TP are depicted in to Control, up to 23.10 mm day -1 (RSME) in SNQ_trop39 for extreme precipitation events. The degraded 210 skills of the SNQ_trop25 and SNQ_trop39 experiments in predicting precipitation were contrary to previous studies where adding spectral nudging toward water vapor mixing ratio could largely reduce the wet bias of precipitation intensity (Spero et al., 2014;. In this assessment, the best performance was achieved by the SNnoT experiment with the lowest RMSE and MAE for different precipitation thresholds. In addition, the SNnoT experiment showed a clear advantage for the extreme precipitation 215 event, reducing the RMSE by 1.16 mm day -1 compared with the Control and 2.27 mm day -1 compared with the SN.

Evaluation of diurnal precipitation
In addition to error metrics, frequency distributions of precipitation intensity and diurnal cycle of the mean precipitation from WRF simulations were compared with CMORPH ( Figure 5). It is clear that 220 precipitation from WRF simulations were heavily overestimated in the occurrence of precipitation events when precipitation intensity exceeds 3 mm day -1 . With respect to Control and other spectral nudging strategies, the frequency distribution of precipitation intensity simulated by the SNnoT experiment was more comparable to CMORPH (Figure 5a). The closer frequency density of high precipitation threshold indicated the advantage of restricting nudging for temperature and water vapor mixing ratio in model 225 may be attributed to decrease the overforecasting of extreme precipitation events over the southern TP.
The diurnal cycle of precipitation is an important feature of the monsoon precipitation because it controls the circulation characteristic and affects the precipitation magnitude (Bhatt et al., 2014;Sato et al., 2008).

Large-scale atmospheric circulation anomalies 250
In summer, precipitation occurred in the TP is mainly controlled by two water vapor channels, one of which is the transported by strong southwesterly under the effect of the Indian summer monsoon. The second channel is transported by the south branch of the mid-latitude westerly. By the influences of both channels, a large amount of AWV from the Bay of Bengal could be transferred the TP (Xu et al., 2008;Xu et al., 2002;Zhou and Li, 2002). 255 The monthly mean AWV transport fields, averaged for the whole atmospheric layer, related to the simulated precipitation are displayed in Figure 6. As represented by ERA5 (Figure 6a), large-scale atmospheric circulation favours strong AWV transport into the southeastern TP, with an AWV transport centre occurring at the windward slope of the southwestern Himalayas. All simulations reproduced such AWV fields over the southern TP but with weaker magnitudes compared to ERA5, in which more AWV 260 is transported to the interior of the TP. Note that not all spectral nudging experiments reduced the wet bias of water vapor transport. Both SNlowU and SNlowT (Figure 6d and 6e) showed comparable results with those of the Control (Figure 6b), which indicated stronger AWV transport over the central and northern TP and overestimated the magnitude of the water vapor transport centre at the Bay of Bengal.
were greatly misrepresented over the remaining regions of the TP, which could have detrimental effects on water cycle analysis. With reference to the above, the AWV transport obtained in SNnoT (Figure 6f) closely approached to that of ERA5, and the wet bias of AWV transport was reduced compared to the other spectral nudging experiments and only indicated a slight overestimation over the western TP. As 270 anticipated from the precipitation anomalies, such a difference in water vapor transport is obviously related to the smaller wet bias of precipitation.
The TP and its surrounding area have been regarded as a favorable region for convective processes because of its high elevation, by which moister can be transferred to the upper layers during summer monsoon season (Fu et al., 2006;Heath and Fuelberg, 2014). Deep convection favors the process of 275 transporting emissions from the surface into the upper-level atmosphere, through which moisture flux is vertically released into the atmospheric and influences the production of precipitation. SNQ_trop25 and SNQ_trop39 (Figure 6g and 6h) showed an analogous performance with smaller water vapor transport over the Bay of Bengal, indicating that the difference between SNQ_trop25 and SNQ_trop39 in precipitation simulations may be attributed to local convection and subsequently processes. 280

Vertical structure of the convective process
The terrain of the southern Himalayas, which has a sharp altitude gradient, is extremely complex.
Atmospheric water vapor transport is impeded, and a strong upward motion is consequently formed at the windward slope caused by the lifting effect of the complex terrain. However, the complex orography of the Himalayas is greatly smoothed in RCMs, even though high horizontal spatial resolutions (e.g. 3 285 km) are applied (Wang et al., 2020). Therefore, the impact of a drag force due to the complex orography on the airflow is weakened, as is the convergence of water vapor. Consequently, more water vapor transport will be transferred to the high-latitude TP, causing more precipitation over the TP. Figure 7 and The strongest upward motion along the latitude was simulated by the SNQ_trop39 (Figure 7g) over the southern slope of the Himalayas, followed by the SN and SNQ_trop25 (Figure 7b and 7f). Large amounts of water vapor from the Bay of Bagel were transferred to the upper troposphere by the strong upward motion, which were converted to the interior of the TP through high-level southwestern advection, subsequently causing more precipitation over those regions. Despite the SNlowU and SNlowT 295 experiments (Figure 7c and 7d) simulating smaller upward wind compared to that of the SN, both patterns of the upward motion were similar with that of the Control (Figure 7a). The upward motion over the southern slope of the Himalayas simulated by the SNnoT (Figure 7e) showed a clear reduction compared to the other experiments, in which the upslope water vapor transport was largely limited by the Himalayas. Therefore, most of the water vapor were condensed during upslope flow over the Himalayas, 300 causing little water vapor available for precipitation over the interior of the TP.
Although the intensity of zonal mean upward motion over the TP is relatively weaker than that of meridional upward motion, a similar discrepancy was also observed in the SNnoT (Figure 8e), which indicated the weakest upward motion compared to the other experiments. Therefore, the convergence of airflow accompanied by water vapor over the central TP was enhanced, leading to a slightly larger 305 https://doi.org/10.5194/gmd-2020-394 Preprint. Discussion started: 29 December 2020 c Author(s) 2020. CC BY 4.0 License. precipitation intensity in the SNnoT (Figure 2h and Figure 6f) than in the other experiments. The strongest upward motion that occurred in the upper layer, along 88-102°E, was simulated by the Control (Figure 8a), followed by the SNQ_trop39, SN and SNQ_trop25 experiments. In the two SNQ simulations, the upward motion decreased when the lid was applied at a lower model level (layer 25, approximately 300 hPa in the model pressure level) (Figure 7f and Figure 8f). Large-scale driving 310 datasets are insufficient to represent the horizontal and vertical variations in moisture and temperature above the tropopause (Miguez-Macho et al., 2004). The lid was originally designed to avoid the transferring moisture and temperature biases above the tropopause in the reference field to the modelsimulated fields. With reference to the above analysis, it is suggested that setting the lid at the tropopause layer in the model is inappropriate when simulating precipitation over the TP. 315

Vertical profile of the atmospheric temperature, water vapor mixing ratio and horizontal wind speed
Dynamical downscaling studies in WRF simulations centred over the TP that use ERA-Interim reanalysis products as the reference fields have ensemble underestimations of atmospheric temperature and overestimations of the water vapor mixing ratio in summer (He et al., 2019;Xu et al., 2017). As the 320 SNnoT experiment tends to exhibit smaller water vapor transport and weaker upward wind over the TP, the mean vertical profiles of atmospheric temperature and the water vapor mixing ratio from 100 to 700 hPa, averaged over the TP, were further investigated. The performances of the spectral nudging experiments were examined against the Control experiment and their differences are shown in Figure 9.
Generally, apparent improvement is observed in several spectral nudging experiments in reducing the 325 cold bias compared to the Control, except that the SN and SNQ_trop39 experiments resulted in larger cold biases in the upper troposphere, while the SNnoT and SNQ_trop25 experiments only had slightly smaller temperatures above 150 hPa (Figure 9a). Although the performances of SNlowU and SNlowT are highly comparable to the Control, both SNlowU and SNlowT simulated higher temperatures at lower levels. This pattern of SNlowU and SNlowT results is also observed in the comparison of the water vapor 330 mixing ratios; both experiments simulated a slightly drier water vapor mixing ratio in the low troposphere ( Figure 9b). The vertical profiles of the temperature difference fields of the SN, SNQ_trop25 and SNQ_trop39 showed similar variations below 300 hPa, but the temperatures in SN and SNQ_trop39 sharply decreased and became colder than those in the Control at higher layers. The vertical profile of SNQ_trop25, however, was aligned with the SNnoT in the middle troposphere but had higher 335 temperature. In terms of the vertical spread of the water vapor mixing ratio (Figure 9b), the higher temperature obtained in SNQ_trop25 is likely attributed to its higher water vapor mixing ratio within the free troposphere, which caused wetter atmosphere conditions; thus, more heat flux was released once precipitation began.
Compared to the Control experiment, the best performance in reducing the wet bias of the water vapor 340 mixing ratio in atmospheric layers was achieved by the SNnoT, despite the SN simulating slightly drier water vapor, with a value of 0.17 k kg -1 at 400 hPa. Both SNQ_trop25 and SNQ_trop39 simulated the wettest water vapor mixing ratio at 500 hPa, but SNQ_trop25 had higher water vapor than did SNQ_trop39 when spectral nudging towards the water vapor mixing ratio was limited above 300 hPa in https://doi.org/10.5194/gmd-2020-394 Preprint. Discussion started: 29 December 2020 c Author(s) 2020. CC BY 4.0 License. the SNQ_trop25 experiment. Based on the above results, spectral nudging towards water vapor mixing 345 ratio in more atmospheric layers will reduce the wet bias of water vapor. However, the improvement could not compensate for the negative impact that adding the ability to a model to perform spectral nudging towards water vapor artificially introduces extra wet bias due to the overestimation of ERA-Interim, when it is used as the reference field, in representing water vapor over the TP. In the study of He In summary, the inter-comparisons of the ensemble model simulations demonstrate that SNnoT achieved 360 notable improvements over the Control and the remaining spectral nudging experiments in water vapor transport, atmospheric temperature estimations and subsequent convective process simulations. The combinations of weakened convective motion over the Himalayan foothills and smaller horizontal wind speed in the free troposphere simulated by the SNnoT experiment limited water vapor to be transported to the interior of the TP, which indicated less moisture available for precipitation. The improved moisture 365 transport and large-scale circulation will have significant implications for precipitation analysis and water cycle budgets over the TP.

Conclusions
In this paper, the impacts and improvements of the spectral nudging technique in the WRF model for 370 simulating precipitation and associated meteorological variables over the TP were evaluated using seven experiments.
Firstly, evaluations against the CMORPH precipitation data indicate that conventional continuous simulation cannot reproduce actual precipitation patterns and largely overestimate precipitation events over the western and northern TP. The use of spectral nudging in the WRF model reduces such 375 overestimations but does not always overcome all deficiencies when simulating the precipitation intensity and its diurnal cycle. Spectral nudging experiments with decreased nudging coefficients (4.5 × 10 -5 s -1 ) for wind and potential temperature showed comparable results with conventional continuous simulation in terms of precipitation, associated water vapor transport and temperature simulations. In addition, allowing spectral nudging towards water vapor mixing ratio in more 380 atmospheric layers within the troposphere can reduce the wet bias of water vapor. This improvement cannot compensate for the artificially introduced extra wet bias when water vapor in the model field is relaxed towards the ERA-Interim reanalysis. Therefore, although the ERA-Interim reanalysis has been https://doi.org/10.5194/gmd-2020-394 Preprint. Discussion started: 29 December 2020 c Author(s) 2020. CC BY 4.0 License. widely used as a large-scale reference field for regional climate studies of the TP in RCMs, its uncertainties when representing atmospheric temperature and water vapor fields over this region should 385 be strongly considered when downscaling the ERA-Interim reanalysis. Following this perspective, the evaluation of vertical temperature profiles implies that the simulations of atmospheric temperature in the model are more sensitive to the cold bias of temperature due to ERA-Interim in the upper troposphere than those in the lower and middle troposphere.
To decrease such biases and balance the accuracy of precipitation, temperature and water vapor 390 simulations, spectral nudging towards potential temperature and the water vapor mixing ratio was restricted in the whole layers (designated SNnoT). The change to spectral nudging has clear advantages over conventional continuous simulation and other spectral nudging experiments and largely improved the precipitation intensity forecast as well as the forecast of its diurnal cycle compared to CMORPH.
Based on subsequent analysis, SNnoT reduced the meridional water vapor transport and upward motion 395 at the southern slope of the Himalayas; thus, less water vapor could reach the upper layers, which caused less precipitation over the southern region and the interior of the TP. Consistently, SNnoT also improved the simulations of atmospheric temperature and the water vapor mixing ratio by collectively alleviating the cold bias of temperature and wet bias of the water vapor.
The evaluation and improvement of the spectral nudging technique in the WRF model in this work not 400 only concentrates on optimizing precipitation forecasts but also aims to increase the reliability of RCM data used to assess the regional climate change without degrading the simulations of temperature and water vapor. The conclusion of this work is also useful for the application of RCMs in dynamical downscaling processes when using different reference fields. Since regional cumulus and land surface processes are also essential in regional climate modelling because of their significant effects 405 on both large-scale and regional atmospheric circulation, improvements in representing such physical processes are required in future studies.
Data availability. The ERA-Interim reanalysis data are available from the European Centre for Medium- Table 1: Nudging coefficients (10 -4 s -1 ) used for spectral simulations. Spectral nudging is indicated by "SN". U, V represent for horizontal wind component, T represents for potential temperature, Q represents for water vapor mixing ratio and represents for geopotential. Nudging is applied to all layers above the PBL; layer 39 represents approximately the mean pressure level of tropopause over the TP; layer 25 represents approximately the normally lower limit for "ktrop" that is set to the middle layer in model simulation;   https://doi.org/10.5194/gmd-2020-394 Preprint. Discussion started: 29 December 2020 c Author(s) 2020. CC BY 4.0 License.