Impact of Assimilating Ground-Based Microwave Radiometer Data on the Precipitation Bifurcation Forecast: a Case Study in Beijing

Article Impact of Assimilating Ground-Based Microwave Radiometer Data on the Precipitation Bifurcation Forecast: A Case Study in Beijing

Yajie Qi 1,2, Shuiyong Fan 1,*, Jiajia Mao 3, Bai Li 3, Chunwei Guo 1 and Shuting Zhang 1

1 Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China; [email protected] (Y.Q.); [email protected] (C.G.); [email protected] (S.Z.) 2 Key Laboratory for Cloud Physics of China Meteorological Administration, Beijing 100081, China 3 Meteorological Observation Center of China Meteorological Administration, Beijing 100081, China; [email protected] (J.M.); [email protected] (B.L.) * Correspondence: [email protected]

Abstract: In this study, the temperature and relative humidity profiles retrieved from five ground- based microwave radiometers in Beijing were assimilated into the rapid-refresh multi-scale analysis and prediction system-short term (RMAPS-ST). The precipitation bifurcation prediction that occurred in Beijing on 4 May 2019 was selected as a case to evaluate the impact of their assimilation. For this purpose, two experiments were set. The Control experiment only assimilated conventional observations and radar data, while the microwave radiometers profilers (MWRPS) experiment assimilated conventional observations, the ground-based microwave radiometer profiles and radar data into the RMAPS-ST model. The results show that in comparison with the Control test, the Citation: Qi, Y.; Fan, S.; Mao, J.; Li, B.; MWRPS test made reasonable adjustments for the thermal conditions in time, better reproducing Guo, C.; Zhang, S. Impact of the weak heat island phenomenon in the observation prior to the rainfall. Thus, assimilating Assimilating Ground-Based MWRPS improved the skills of the precipitation forecast in both the distribution and the intensity of Microwave Radiometer Data on the rainfall precipitation, capable of predicting the process of belt-shaped radar echo splitting and the Precipitation Bifurcation Forecast: A precipitation bifurcation in the urban area of Beijing. The assimilation of the ground-based microwave Case Study in Beijing. Atmosphere radiometer profiles improved the skills of the quantitative precipitation forecast to a certain extent. 2021, 12, 551. https://doi.org/ Among multiple cycle experiments, the onset of 0600 UTC cycle closest to the beginning of rainfall 10.3390/atmos12050551 performed best by assimilating the ground-based microwave radiometer profiles.

Academic Editor: Sergio Fernández-González Keywords: ground-based microwave radiometer; weak heat island effect; the precipitation bifurcation

Received: 29 March 2021 Accepted: 21 April 2021 Published: 25 April 2021 1. Introduction Ground based microwave radiometer is meteorological observation instrument based Publisher’s Note: MDPI stays neutral on remote sensing technology. Its primary data are brightness temperature data, which with regard to jurisdictional claims in represents electromagnetic wave signal received by radiometer at specified frequency. They published maps and institutional affil- are a kind of unconventional observation data. The secondary data of atmospheric temper- iations. ature and humidity profiles and cloud and precipitation information are obtained through inversion calculation. So far, level-2 of ground-based microwave radiometer products have provided continuous temperature and humidity profiles at a high time frequency [1–3], providing valuable information to follow the evolution of the boundary layer. Copyright: © 2021 by the authors. Its products have been extensively applied to many fields, such as liquid water Licensee MDPI, Basel, Switzerland. path inversion for weather modification, boundary layer height inversion for air quality This article is an open access article monitoring and prediction [4,5], water vapor characteristics analysis, and environmental distributed under the terms and meteorological conditions for haze maintenance [6–9]. The continuous and stable tem- conditions of the Creative Commons perature and humidity profiles provided by the ground-based microwave radiometer Attribution (CC BY) license (https:// effectively have overcome the deficiency of the conventional radiosonde in obtaining at- creativecommons.org/licenses/by/ mospheric information due to the long observation interval. Previous studies have shown 4.0/).

Atmosphere 2021, 12, 551. https://doi.org/10.3390/atmos12050551 https://www.mdpi.com/journal/atmosphere Atmosphere 2021, 12, 551 2 of 18

that the ground-based microwave radiometer can be assimilated and applied to numerical prediction and that it has different impacts on the prediction. The ground-based microwave radiometer observations have been playing an increas- ingly important role in numerical weather prediction systems. However, the assimilation of the microwave radiometers into numerical weather prediction systems has been limited to a few sporadic attempts [10]. For example, Vandenberghe and Ware (2002) assimilated the temperature and humidity profile data of the single station ground-based microwave radiometer into MM5, effectively improved the prediction of winter fog [11]. Assimilating the ground-based microwave radiometer data into WRF had a positive impact on the prediction of temperature and humidity analyses, whereas the impact on precipitation accumulation forecasts was more obvious [12,13]. Caumont et al. (2016) assimilated the profiles retrieved by multiple ground-based microwave radiometers into the mesoscale prediction system, the results showed that the impact was neutral and only works for large-scale precipitation [12]. In a rainstorm process, the relative humidity profiles retrieved by three ground-based microwave radiometers were assimilated, and the results show that the prediction of precipitation intensity was significantly improved, but that of precipitation distribution was not [14]. Data of two ground-based microwave radiometers were assimilated rainstorm in Beijing on in July 20 in 2016. It proved that the microwave radiometers data assimilation improved the prediction of the intensity and distribution of precipitation in its early stage [15]. It is very challenging to obtain high-quality precipitation estimates. For blending multi-satellite, atmospheric reanalysis, and gauge precipitation products, the distribution of observations are unevenly and sparsely and the precipitation estimates based solely on observations are subject to large uncertainties [16,17]. The machine learning algorithms have been applied to merge Satellite-based precipitation products and observations to improve the accuracy of the precipitation estimates in recent years. For instance, Kumar (2019) corrected the near-real-time multi-satellite precipitation analysis-based product by combining it with soil moisture through a support vector machine based regression model [18]. Bhuiyan et al. (2020) employed the random forest and neural networks to generate an error-corrected product [19]. Zhang et al. (2021) proposed a novel double machine learning approach to merge multiple satellite-based precipitation products and observations [17]. Yagmur (2020) developed tree-based quantile regression to reduce precipitation retrieval error [20]. For the accuracy of precipitation forecasts produced by a numerical model, it is greatly influenced by the initial states of the atmosphere, especially for the short-term forecast system [21]. The prediction of a definite numerical model is subject to the accuracy of its initial field. From this view, there is a particular need for more in-situ observations on the profiles of atmospheric meteorological element. These are important for initialization of numerical weather prediction models [22–24]. Data assimilation combines the observation data with the background field of the model to produce more accurate initial values, which will contribute to improve the meteorological element prediction and the development of the precipitation system [25–27]. The variational method considers the system as a whole and combines the model and observations in a statistically optimal sense [27]. It is currently widely used in research communities and operational centers. The WRF data assimilation system WRFDA included the three-dimensional variation, four-dimensional variation, and hybrid techniques at the National Center for Atmospheric Research [28–30]. The three-dimensional variation method is often used in the assimilation filed, owing to its low calculation cost, small resource occupation, and high efficiency. The 3DVAR framework is generally easier for implementation in operational runs. Previous studies have made insightful exploration into the application of ground- based microwave radiometer data in the numerical model. However, few researchers focused on the application of assimilating ground-based microwave radiometer data to regional operational forecast over North China. In particular, for Beijing, being embraced on three sides by mountains, the urban heat island effect, coupled with other factors, Atmosphere 2021, 12, 551 3 of 18

increase the difficulty and uncertainty of its precipitation forecast. Currently, although the weather-forecasting technology is capable of forecasting rainfall caused by large-scale weather systems, it is incapable of forecasting local rainfall and does not perform as required with respect to people’s life and production activities, especially in the urban area [31,32]. Thus, reproducing observed urban effect on rainfall can help increase the accuracy of rainfall forecasting in Beijing. In this study, the temperature and humidity profiles retrieved from five ground- based microwave radiometers in Beijing are assimilated into the rapid-refresh multi-scale analysis and prediction system–short term (RMAPS-ST). The impact of the ground-based microwave radiometers data on the analyses and forecasts of this splitting process in Beijing are evaluated. Assimilation experiments are carried out in this study. Combined with comparative analysis, we explore the effect of assimilation of ground-based microwave radiometer data on the prediction of the precipitation bifurcation in Beijing. Through the experiments, we aim to advance urban fine weather forecast to serve urban operation while helping public production with their daily activities by providing better information. The rest of this paper is structured as follows. Section2 introduces the belt-shaped echo splitting case examined for this study, the characteristics of ground-based microwave radiometer data and the experimental setting. In Section3, the impact of assimilated ground-based microwave radiometer data on both analysis field and the prediction of belt- shaped convection splitting are compared, for the continuously cycling data assimilation and forecasting experiments. The diagnosis for this rainfall bifurcation event with and without the data assimilation of the ground-based microwave radiometers are discussed in Section4. Finally, conclusions are presented in Section5.

2. Materials and Methods 2.1. Belt-Shaped Echo Splitting Case In the present study, we take the echo splitting process in Beijing on 4 May 2019 as an example to investigate whether the assimilation of ground-based microwave radiometer data could improve precipitation simulation. The evolution of belt-shaped echo in this process is shown in Figure1. At 0400 UTC (Coordinated Universal Time) on 4 May 2019, a northeast and southwest belt-shaped echo emerges in Inner Mongolia, followed by convection. The strong echo belt moves rapidly to the southeast, reaches Beijing at 0900 UTC, and splits at 1000 UTC when it approaches the urban area of Beijing. Within the urban area, the echo weakens and dissipates. The echo in the northern and southern parts of the urban area continues to move southeast before it disappears at 1100 UTC. The weather process spanned a period of 7 h. It is characterized by the belt-shaped echo splitting in the periphery of Beijing, during which no precipitation occurs in the urban area of Beijing when it moves from northwest to southeast (Figure1).

2.2. Data Processing of Ground-Based Microwave Radiometer A total of seven microwave radiometers are deployed in Beijing during the metropoli- tan observation experiments. Data of the two microwave radiometers deployed in Haidian District and Shangdianzi Village (in Miyun District) is missing due to equipment problems, while that of the remaining five microwave radiometers deployed in Xiayunling Village (in Fangshan District), Yanqing District, southern suburb, Huairou District, and Pinggu District is available. The five level-2 ground-based microwave radiometer products in Beijing are obtained by the inversion software from microwave radiometer manufacturers, including the temperature and relative humidity profiles. The continuous observations of temperature and humidity profiles are high temporal resolution (at a high frequency rate up to two minutes). Atmosphere 2021, 12, x FOR PEER REVIEW 4 of 19 Atmosphere 2021, 12, 551 4 of 18

Figure 1. Radar composite reflectivity (CREF) evolution for belt-shaped zone in North China on 4 May 2019. Figure 1. Radar composite reflectivity (CREF) evolution for belt-shaped zone in North China on 4 May 2019. Figure2 shows the vertical distribution of retrieved temperature and humidity profiles 2.2. Data Processing of Ground-Based Microwave Radiometer at each station, as well as their evolution with time. It reveals that microwave radiometers overcomeA total the of spatialseven microwave and temporal radiometers shortcomings are ofdeployed conventional in Beijing observation, during the especially metro- politantemporal observation ones. Prior experiments. to the three-dimension Data of the variation two microwave assimilation, radiometers the temperature deployed profile in Haidianand relative District humidity and Shangdianzi profile data Village retrieved (in byMiyun microwave District) radiometer is missing due at 0–10 to equipment km height problems,are processed. while Since that precipitationof the remaining has greatfive microwave impact on temperatureradiometers anddeployed relative in humidity Xiayun- lingretrieved Village by (in microwave Fangshan radiometer, District), theYanqing observation District, data southern of radiometer suburb, during Huairou precipitation District, andshould Pinggu be prudently District is dealtavailable. with. The In thisfive paper,level-2 theground-based data at the microwave corresponding radiometer time of productsprecipitation in Beijing are set are as missing obtained value. by the In addition,inversion as software the temperature from microwave and humidity radiometer profiles manufacturers,retrieved by microwave including radiometers the temperature at 0–10 and km relative height humidity are of high profiles. vertical The resolution, continuous the observationsreference atmospheric of temperature pressure and athumidity each height profiles layer are from high 0 temporal to 10 km resolution is calculated (at a by high the frequencyformula [33 rate]. up to two minutes). Figure 2 shows the vertical distribution of retrieved temperature and humidity profiles at each station, as well as their evolution with time. It reveals that microwave radiometers overcome the spatial and temporal shortcomings of conventional observation, especially temporal ones. Prior to the three-dimension variation assimilation, the temperature profile and relative humidity profile data retrieved by microwave radiometer at 0– 10 km height are processed. Since precipitation has great impact on temperature and relative humidity retrieved by microwave radiometer, the observation data of radiometer during precipitation should be prudently dealt with. In this paper, the data at the corresponding time of precipitation are set as missing value. In addition, as the temperature and humidity profiles retrieved by microwave radiometers at 0–10 km height are of high

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Atmosphere 2021, 12, 551 5 of 18 vertical resolution, the reference atmospheric pressure at each height layer from 0 to 10 km is calculated by the formula [33].

Figure 2. Time-heightFigure 2. profileTime-height of temperature profile of andtemperature humidity and profile humi ofdity five profile ground-based of five ground-based microwave radiometers micro- in Beijing on 4 May 2019wave and radiometers the judgment in of Beijing precipitation on 4 May at corresponding2019 and the judgment time from of theprecipitation MWRPS located at corresponding in Xiayunling; Yanqing, Pinggu, Huairou,time from andSouthern the MWRPS suburb located ofBeijing. in Xiayunling; Note: theYanqing, blue bar Pinggu, indicates Huairou, the period and Southern of rainin suburb the ground-based of Beijing. Note: the blue bar indicates the period of rain in the ground-based microwave radiometer microwave radiometer observations. observations.

2.3. Experiment2.3. Design Experiment Design In this study,In an this experiment study, an is experiment carried out, is carriedusing RMAPS-ST out, using numerical RMAPS-ST forecast numerical forecast model. The model, based on the previous generation of North China rapid-refresh cyclic model. The model, based on the previous generation of North China rapid-refresh cyclic assimilation and forecast system [34,35] is a short-term forecasting subsystem of a new assimilation and forecast system [34,35] is a short-term forecasting subsystem of a new generation of RMAPS developed by Institute of Urban Meteorology, CMA, Beijing. It generation of RMAPS developed by Institute of Urban Meteorology, CMA, Beijing. It has has been in operation since May 2017 [36]. The RMAPS-ST features double nesting, nine- been in operation since May 2017 [36]. The RMAPS-ST features double nesting, nine-kilo- kilometer-resolution outermost D01 area with 649 × 500 grid points covering the whole meter-resolution outermost D01 area with 649 × 500 grid points covering the whole China, China, and three-kilometer-resolution inner D02 area with the innermost 550 × 424 grid and three-kilometer-resolution inner D02 area with the innermost 550 × 424 grid points in points in the simulated area covering North China. The parameterized schemes of main the simulated area covering North China. The parameterized schemes of main physical physical process of the experiment include a new Thompson cloud microphysics scheme, process of the experiment include a new Thompson cloud microphysics scheme, NOAH NOAH land surface scheme, Yonsei University (YSU) boundary layer scheme [37], the land surface scheme, Yonsei University (YSU) boundary layer scheme [37], the global pa- global parameterization of the Rapid Radiative Transfer Model (RRTMG) scheme [38,39]. rameterization of the Rapid Radiative Transfer Model (RRTMG) scheme [38,39]. In this In this paper, ECMWF medium-range forecast (0.25◦ × 0.25◦) is selected to provide the paper, ECMWFinitial medium-range field and side forecast boundary (0.25° conditions × 0.25°) is selected for the model.to provide Having the initial gone field through quality and side boundarycontrol, conditions the observed for the data, model. including Having conventional gone through data quality and radar control, data, the are input into observed data,the including assimilation conventional system. Most data ofand these radar conventional data, are input data into are the collected assimilation every hour. Radar system. Mostdata of these are collectedconventional at the data frequency are collected rate ofevery six minuteshour. Radar and data the radarare collected data assimilation is at the frequencyperformed, rate of six including minutes radial and the velocity radar anddata reflectivity. assimilation Owing is performed, to low calculation includ- cost, small ing radial velocityresource and occupation reflectivity. and Owing high efficiency,to low calculation the three-dimensional cost, small resource variational occupa- data assimilation tion and highsystem efficiency, (3DVar) the of three-dimensio the Weather Researchnal variational and Forecasting data assimilation model system system is adopted. The (3DVar) of thesolution Weather of 3DVarResearch can and be interpreted Forecasting as model obtaining system the minimizationis adopted. The of thesolution objective function. of 3DVar canBased be interpreted on the optimization as obtaining theory,the minimization the optimal of the solution objective is obtained function. by Based iterative descent on the optimizationalgorithm. theory, Specifically, the optimal the optimal solution state is obtained of the atmosphere by iterative is estimated descent algo- by using both the rithm. Specifically,background the optimal field and state observed of the atmosphere values, thus is the estimated statistical by optimal using both analysis the is obtained. background Whenfield and U and observed V are usedvalues, as dynamicthus the controlstatistical variables, optimal the analysis correlation is obtained. between variables is When U and smallerV are used than as that dynamic when control traditional variables, flow function the correlation and potential between function variables control is variables smaller than arethat used, when which traditional satisfies flow the func assumptiontion and of potential variational function dataassimilation. control variables Such algorithm is are used, whichmore satisfies conducive the assumption to the description of variational of medium data andassimilation. small-scale Such systems algorithm [40]. Background is more conduciveerror covarianceto the description is calculated of medi byum the and National small-scale Meteorological systems [40]. Center Background (NMC) method [41]. The system runs eight times a day at 0000 UTC, 0300 UTC, 0600 UTC, 0900 UTC, 1200 UTC, 1500 UTC, 1800 UTC, and 2100 UTC, respectively. At 0000 UTC, the system starts in the

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Atmosphere 2021, 12, 551 error covariance is calculated by the National Meteorological Center (NMC) method6 [41]. of 18 The system runs eight times a day at 0000 UTC, 0300 UTC, 0600 UTC, 0900 UTC, 1200 UTC, 1500 UTC, 1800 UTC, and 2100 UTC, respectively. At 0000 UTC, the system starts in the mode of Partial Cycle [42], that is, the initial field is the 6-h forecast field of 1800 UTC mode of Partial Cycle [42], that is, the initial field is the 6-h forecast field of 1800 UTC the the previous day; afterwards the initial field is driven by the 3-h forecast field of the pre- previous day; afterwards the initial field is driven by the 3-h forecast field of the previous vious time level. time level. In total, two groups of experiments are designed, that is, the Control experiment and In total, two groups of experiments are designed, that is, the Control experiment and the MWRPS experiment. The Control experiment assimilates conventional observation the MWRPS experiment. The Control experiment assimilates conventional observation data anddata Beijing–Tianjin–Hebei and Beijing–Tianjin–Hebei weather weather radar data. radar Different data. Different types of conventionaltypes of conventional observations ob- areservations incorporated are incorporated into the system into the to system improve to improve the analysis the analysis for both for domains, both domains, including in- aircraftcluding meteorological aircraft meteorological data relay, data synoptic, relay, sounding,synoptic, sounding, oceanographic oceanographic buoys, and buoys, ship-based and observationsship-based observations (Figure3). Radar(Figure data 3). Radar assimilation data assimilation are mainly are in mainly Domain in 2 Domain of the system, 2 of the includingsystem, including radial velocity radial andvelocity reflectivity. and reflectivity. Based on Based the Control on the experiment, Control experiment, the MWRPS the experimentMWRPS experiment adds the dataadds of the five data microwave of five mi radiometerscrowave radiometers in Beijing. The in Beijing. data distribution The data isdistribution shown in Figureis shown3. We in compareFigure 3. theWe differencescompare the between differences the experimentalbetween the experimental results with andresults without with and the ground-basedwithout the ground-based microwave radiometer microwave data radiometer assimilation data for assimilation analysis. for analysis.

Figure 3. Spatial distribution of the assimilated observations at 0000 UTC. (a) In Domain 1 of RMAPS-ST, locations of radiosonde launch sites used are represented as purple solid circles, oceanographic buoys and ship-based observations are Figure 3. Spatial distribution of the assimilated observations at 0000 UTC. (a) In Domain 1 of RMAPS-ST, locations of represented as red circles and green circles, Dark blue circles and pink circles indicate, respectively, synoptic and aircraft radiosonde launch sites used are represented as purple solid circles, oceanographic buoys and ship-based observations meteorologicalare represented data as red relay circles whenever and green available circles, at 0000Dark UTC blue 4 circles May 2019. and (pinkb) Domain circles 2indicate, and the radarrespectively, locations synoptic marked and as darkaircraft blue meteorological solid circles. ( cdata) Locations relay whenever of MWRPS available sites used at in0000 this UTC study 4 areMay also 2019. shown, (b) Domain represented 2 and as blackthe radar diamond. locations marked as dark blue solid circles. (c) Locations of MWRPS sites used in this study are also shown, represented as black diamond. Each group of experiments performs a cold start from 1800 UTC on 3 May 2019, and predicted for six hours to obtain the forecast field at 0000 UTC on 4 May 2019, which is used as the background field to assimilate the observed data. Then, 0300 UTC is driven by the 3-h forecast field of 0000 UTC, and the 0600 UTC experiment is driven by the 3-h forecast field of 0300 UTC (see Figure4 for the test flow). The two-step assimilation

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Each group of experiments performs a cold start from 1800 UTC on 3 May 2019, and Atmosphere 2021, 12, 551 predicted for six hours to obtain the forecast field at 0000 UTC on 4 May 2019, which7 of 18 is used as the background field to assimilate the observed data. Then, 0300 UTC is driven by the 3-h forecast field of 0000 UTC, and the 0600 UTC experiment is driven by the 3-h forecast field of 0300 UTC (see Figure 4 for the test flow). The two-step assimilation methodmethod of of RMAPS-ST RMAPS-ST is is carried carried forward. forward. In In the the ﬁrst first step, step, conventional conventional data data and and ground- ground- basedbased microwave microwave radiometer radiometer data data are are assimilated; assimilated; in in the the second second step, step, radar radar observations observations areare assimilated. assimilated.

Figure 4. A schematic of data assimilation and forecast experiments in the RMAPS-ST. Figure 4. A schematic of data assimilation and forecast experiments in the RMAPS-ST.

3.3. Results Results 3.1.3.1. Impact Impact of of Assimilated Assimilated Ground-Based Ground-Based Microwave Microwave Radiometer Radiometer Data Data on on Analysis Analysis Field Field DataData assimilation assimilation providesprovides thethe initial field field for for the the numerical numerical model, model, which which makes makes the thecalculation calculation results results of ofthe the model model more more accurate. accurate. To To verify verify the the impact impact of ofMWRPS MWRPS data data as- assimilationsimilation on on the the initial initial field, field, we we take take the the initial initial report report test test at at0600 0600 UTC UTC as asan anexample example and andcompare compare the the temperature temperature and and humidity humidity profil profileses of of observation, backgroundbackground fieldfield and and analysisanalysis field field at at Station Station 54419 54419 (Shangdianzi) (Shangdianzi and) and Station Station 54511 54511 (southern (southern suburb) suburb)(Figure (Figure5). At5). 0600 At 0600 UTC UTC (1400 (1400 Beijing Beijing time), time), at Stationat Station 54419 54419 the the near-surface near-surface air air temperature temperature is is slightlyslightly lower lower and and the the near-surface near-surface specific specific humidity humidity is slightly is slightly higher higher than than those those at Station at Sta- 54511.tion 54511. Compared Compared with the with background the background field, thefield, analysis the analysis field of field assimilation of assimilation experiment exper- ofiment ground-based of ground-based microwave microwave radiometer radiometer data is data closer is closer to the to observation the observation in almost in almost all atmosphericall atmospheric layers. layers. For For example, example, the the temperature temperature in thein the lower lower layer layer below below 850 850hpa hpa at at StationStation 54419 54419 is is high,high, and that in in the the upper upper layer layer at at Station Station 54511 54511 is low. is low. The The analysis analysis field fieldcorrected corrected the thewarm warm bias bias in inthe the lower lower layer layer an andd the the cold cold bias in thethe upperupper layerlayer of of the the backgroundbackground field, field, which which was was closer closer to to the the observation. observation. For For the the specific specific humidity humidity of of the the wholewhole atmosphere, atmosphere, its backgroundits background field field is dry is in dry the bottomin the bottom layer and layer layers and above layers 700 above hpa, wet in middle and lower layers at both Station 54419 and Station 54511. The specific 700hpa, wet in middle and lower layers at both Station 54419 and Station 54511. The spe- humidity of the assimilated ground-based MWRPS test also corrects the deviation, which cific humidity of the assimilated ground-based MWRPS test also corrects the deviation, was closer to the observation. The temperature and humidity profiles of the analysis fields which was closer to the observation. The temperature and humidity profiles of the analy- in the other three ground-based MWRPS stations, compared with those of the background sis fields in the other three ground-based MWRPS stations, compared with those of the field, are closer to the observations (figure omitted). Overall, MWRPS data can improve background field, are closer to the observations (figure omitted). Overall, MWRPS data the similarity between analysis field and the observation field in terms of temperature and can improve the similarity between analysis field and the observation field in terms of humidity profiles. temperature and humidity profiles.

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Figure 5. (a,b) Vertical profiles of the temperature profiles (K) from observation (black lines), background (blue lines) and Figure 5. (a,b) Vertical profiles of the temperature profiles (K) from observation (black lines), background (blue lines) and −1 analysisanalysis (red (red lines) lines) at at verification verification station station (54419, (54419, 54511); 54511); and and (c ,(dc,)d the) the specific specific humidity humidity profiles profiles (g (g kg kg−)1) at at analysis analysis time time (0600(0600 UTC, UTC, 4 4 May May 2019). 2019). 3.2. Impact of Ground-Based Microwave Radiometer Data Assimilation on Belt-Shaped Convection Splitting3.2. Impact Prediction of Ground-Based Microwave Radiometer Data Assimilation on Belt-Shaped Convection Splitting Prediction Based on the analysis field formed by assimilation in the two experiments in Section 3.1, we compareBased on the the prediction analysis field results formed after by assimilation. assimilation For in the the two phenomenon experiments of in the Section belt- shaped3.1, we echo compare splitting the inprediction Beijing on results 4 May after 2019, assimilation. the radar reflectivity For the phenomenon simulations at of 1000UTC the belt- ofshaped the two echo groups splitting of experiments in Beijing are on compared. 4 May 2019, Figure the6 shows radar thereflectivity simulated simulations radar reflec- at tivity1000UTC in the of MWRPS the two andgroups Control of experiments experiments, are the compared. left side showsFigure the6 shows simulation the simulated results obtainedradar reflectivity from the MWRPSin the MWRPS case, initialized and Control at 0000 experiments, UTC, 0300 the UTC, left andside 0600shows UTC, the respec-simula- tively,tion results whereas obtained the right from side the shows MWRPS the simulation case, initialized results at obtained 0000 UTC, from 0300 the UTC, Control and case. 0600 Generally,UTC, respectively, the radar whereas echo zone the correspondsright side shows to the the location simulation of a results convection obtained cell andfrom the the radarControl reflectivity case. Generally, intensity the corresponds radar echo to zone the convection corresponds intensity to the location in the convection of a convection cell. cell Theand three-cyclethe radar reflectivity prediction intensity in the Control corresp testonds has noto the prediction convection ability intensity for the in observed the con- belt-shapedvection cell. echo splitting phenomenon, neither could the MWRPS test be valid from 0000 UTC.The three-cycle In contrast, prediction the MWRPS in teststhe Control valid from test 0300 has UTCno prediction and 0600 UTCability could for the better ob- simulateserved belt-shaped the observed echo belt-shaped splitting convection phenomen splittingon, neither process: could the the MWRPS MWRPS test test produces be valid satisfactoryfrom 0000 UTC. simulation In contrast, results the in termsMWRPS of the tests location valid from and intensity 0300 UTC of and the convection0600 UTC could cells whenbetter the simulate system affectsthe observed the urban belt-shaped area of Beijing. convection Accordingly, splitting it could process: obviously the MWRPS has better test approachingproduces satisfactory prediction simulation ability when results belt-shaped in terms echo of the splitting location occurs. and intensity For theprediction of the con- abilityvection of cells both when the intensity the system and affects the range the urban of the area simulated of Beijing. radar Accordingly, reflectivity, it 0300 could UTC ob- andviously 0600 has UTC better test inapproaching MWRPS were prediction quite closer ability to when the actual belt-shaped situation, echo compared splitting tooccurs. the MWRPSFor the prediction experiment ability valid of from both 0000 the UTC.intensity and the range of the simulated radar reflectivity,The 0300 development UTC and of0600 the UTC convection test in cellsMWRPS in a rainfallwere quite system closer directly to the impact actual the situation, devel- opmentcompared of precipitation. to the MWRPS Both experiment the intensity valid and from distribution 0000 UTC. of rainfall are closely related to the intensity of the nearby convection cells. To examine the improvement in precipitation forecast, the 1-h accumulated precipitation from the observation, MWRPS experiment and Control experiment was compared (Figure7). By comparing the precipitation forecast through observation and two experiments, we find that the assimilated data of the ground-based microwave radiometers are superior to the Control test in 1-h accumulated precipitation simulation, testified mainly in the following aspects.

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Figure 6. Composite radar reﬂectivity (unit: dBZ) of 1000 UTC on 4 May 2019 simulated by MWRPS test (ﬁrst column) and Figure 6. Composite radar reflectivity (unit: dBZ) of 1000 UTC on 4 May 2019 simulated by MWRPS test (first column) Controland Control test (secondtest (second column) column) valid valid from 0000from UTC,0000 0300UTC, UTC, 0300 0600UTC, UTC 0600 from UTC top from to bottomtop to bottom (Beijing–Tianjin–Hebei (Beijing–Tianjin–Hebei region), asregion), labeled as overlabeled the over upper the left upper corner left of corner each subplot. of each subplot.

The development of the convection cells in a rainfall system directly impact the development of precipitation. Both the intensity and distribution of rainfall are closely related to the intensity of the nearby convection cells. To examine the improvement in precipitation forecast, the 1-h accumulated precipitation from the observation, MWRPS experiment and Control experiment was compared (Figure 7). By comparing the precipitation forecast through observation and two experiments, we find that the assimilated data of the ground-based microwave radiometers are superior to the Control test in 1-h accumulated precipitation simulation, testified mainly in the following aspects.

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FigureFigure 7.7. DistributionDistribution of forecastsforecasts ofof 1-h1-h accumulatedaccumulated precipitationprecipitation betweenbetween 1000 UTCUTC andand 11001100 UTCUTC onon 44 MayMay 20192019 estimatedestimated by observation based based on on ground ground stations stations (a ();a );forecasted forecasted by byMWRPS MWRPS simulations simulations initialized initialized at 0000 at 0000 UTC UTC (b), 0300 (b), 0300UTC UTC(d), 0600 (d), 0600UTC UTC(f) on (f 4) onMay 4 May2019; 2019; forecasted forecasted by Control by Control simulations simulations initialized initialized at 0000 at 0000UTC UTC(c), 0300 (c), 0300UTC UTC(e), 0600 (e), 0600UTC( UTCg) on (g 4) May on 4 May2019. 2019. Firstly, the belt-shaped echo splits in the periphery of Beijing at 1000 UTC, and no Firstly, the belt-shaped echo splits in the periphery of Beijing at 1000 UTC, and no precipitation is reported in Beijing from the observation (Figure7a). Compared with the precipitation is reported in Beijing from the observation (Figure 7a). Compared with the Control test, the MWRPS test could deliver prediction closer to the actual condition and Control test, the MWRPS test could deliver prediction closer to the actual condition and better in the forecast of precipitation distribution. Secondly, the hourly precipitation inten- sitybetter predicted in the forecast by the MWRPSof precipitation test assimilating distribution. the ground-basedSecondly, the hourly microwave precipitation radiometer in- datatensity are predicted closer to theby the observation MWRPS (Figuretest assimi7f).lating Regarding the ground-based the prediction microwave of the precipitation radiome- intensityter data are of Baoding,closer to Hebeithe observation Province valid (Figure from 7f). 0600 Regarding UTC, the the MWRPS prediction test showsof the precipi- a result consistenttation intensity with theof Baoding, observation Hebei while Province the Control valid test from reports 0600 UTC, a result the one MWRPS level higher test shows than thea result observation consistent (Figure with7 f,g).the observation Thirdly, as canwhile be seenthe Control from Figure test reports7b,d,f, thea result precipitation one level positionshigher than predicted the observation by the MWRPS (Figure tests 7f,g). valid Thir fromdly, bothas can 0300 be UTCseen andfrom 0600 Figure UTC 7b,d,f, are quite the consistentprecipitation with positions the observation, predicted provingby the MWRPS that the tests prediction valid from at the both above 0300 two UTC time and levels 0600 couldUTC are represent quite consistent the precipitation with the bifurcation observation, phenomenon proving that in the Beijing. prediction The simulation at the above of precipitationtwo time levels intensity could represent by the MWRPS the precipitation test valid from bifurcation 0600 UTC phenomenon is the closest in toBeijing. the obser- The vation.simulation Especially, of precipitation in Baoding, intensity the intensity by the MWRPS center, the test precipitation valid from 0600 intensities UTC is the predicted closest byto the the observation. MWRPS test Especially, valid from in 0000 Baoding, UTC and the intensity 0300 UTC center, are higher the precipitation than the observations. intensities Thepredicted precipitation by the MWRPS predicted test by valid the test from valid 0000 from UTC 0300 and UTC 0300 ranges UTC are from higher 5 mm than h−1 theto 10observations. mm h−1 (Figure The 7precipitationd). predicted by the test valid from 0300 UTC ranges from 5mm.hThe−1 to MWRPS 10mm.h test−1 (Figure valid from 7d). 0600 UCT displays the strongest simulation impact on precipitation bifurcation in Beijing caused by belt-shaped convection splitting. Overall, the forecast precipitation in MWRPS during the assimilation period shows signiﬁcant im-

Atmosphere 2021, 12, x FOR PEER REVIEW 11 of 19

The MWRPS test valid from 0600 UCT displays the strongest simulation impact on precipitation bifurcation in Beijing caused by belt-shaped convection splitting. Overall, the forecast precipitation in MWRPS during the assimilation period shows significant improvement compared with the Control experiment. Both the composite reflectivity and the 1-h accumulated precipitation of the observation, Control, and MWRPS experiment were compared, showing the improvement in the forecast with the ground-based microwave radiometer data assimilation. Therefore, the subsequent analysis is centered around two groups of experiments valid from 0600 UTC.

Atmosphere 2021, 12, 551 3.3. Impact of Ground-Based Microwave Radiometer Data Assimilation on Meteorological11 of 18 Element Prediction before Belt-Shaped Convection Splitting Owing to the joint effects of topography and urban thermal circulation, the urbani- provementzation process compared was significant with the Control in Beijing–Tianjin–Hebei experiment. Both the collaborative composite reﬂectivitydevelopment and area the 1-h[43], accumulated precipitation precipitation in Beijing has of theits uniqueness observation, and Control, complexity and MWRPS [44–46]. experiment Previous studies were compared,have shown showing that the theintensity improvement of urban in heat the island forecast prior with to thethe ground-basedstart of rainfall microwave could pro- radiometerject, to a certain data assimilation.extent, the thermodynamic Therefore, the subsequentimpact of urban analysis underlying is centered surface around on two the groupsprecipitation of experiments process. Under valid from the effect 0600 UTC.of strong heat island, the thermodynamic process prevails, making precipitation concentrate in urban areas. Under the effect of weak heat 3.3. Impact of Ground-Based Microwave Radiometer Data Assimilation on Meteorological Element island, urban dynamics prevails. Hence, precipitation bifurcation takes place, making pre- Prediction before Belt-Shaped Convection Splitting cipitation mainly distributed in the upwind direction of the city and on both sides of the city [43,47,48].Owing to theThus, joint the effects heat ofisland topography intensity and prior urban to the thermal start of circulation, rainfall determines the urbaniza- the tionkind process of urban was effects signiﬁcant on rainfall. in Beijing–Tianjin–Hebei collaborative development area [43], precipitation in Beijing has its uniqueness and complexity [44–46]. Previous studies have Figure 8 shows the spatial distribution of 2m air temperature observed at 09: 00 UTC shown that the intensity of urban heat island prior to the start of rainfall could project, before the echo moved to the urban area in Beijing. Referring to Zhang et al. (2017), we to a certain extent, the thermodynamic impact of urban underlying surface on the pre- select black rectangular boxes to identify the heat island intensity and make statistical cipitation process. Under the effect of strong heat island, the thermodynamic process analysis. At 0900 UTC, this region, including urban and suburban areas in Beijing, report prevails, making precipitation concentrate in urban areas. Under the effect of weak heat no precipitation. It can be seen that before the belt-shaped echo moves to the urban area island, urban dynamics prevails. Hence, precipitation bifurcation takes place, making of Beijing, there is no obvious difference between the urban temperature in the Fifth Ring precipitation mainly distributed in the upwind direction of the city and on both sides of Road and that in its surrounding area. The heat island intensity in Beijing is weak [43]. the city [43,47,48]. Thus, the heat island intensity prior to the start of rainfall determines As indicated by the spatial distribution of 2m temperature bias at 0900 UTC predicted the kind of urban effects on rainfall. by the two groups of experiments (Figure 9a,b), the temperature deviation predicted by Figure8 shows the spatial distribution of 2m air temperature observed at 09: 00 UTC the Control test in and around Beijing’s Fifth Ring Road (black rectangular frame) is larger before the echo moved to the urban area in Beijing. Referring to Zhang et al. (2017), we selectthan that black by rectangularthe MWRPS boxes test. Especially, to identify the the Control heat island test overestimates intensity and the make observed statistical heat analysis.island intensity At 0900 in UTC, the urban this region, area within including the Fifth urban Ring and Road. suburban The areasassimilated in Beijing, data report of the noground-based precipitation. microwave It can be seenradiometers that before corrected the belt-shaped the warm echobias in moves this area, to the and urban the mod- area ofified Beijing, indexes there Bias is no obvious− Bias difference betweenof 2m air the temperature urban temperature are all negative, in the Fifth which Ring is MWRPS Control Roadcloser and to the that observation in its surrounding (Figure area.9c). The heat island intensity in Beijing is weak [43].

Figure 8. Spatial distribution of observed air temperature at 2 m (unit: ◦C above the ground of 0900 UTC on 4 May 2019. Note: the black rectangle box indicate the area (116.12◦ E–116.79◦ E, 39.65◦ N–40.115◦ N) in which temperature prior to the start of rainfall is statistically analyzed.

As indicated by the spatial distribution of 2m temperature bias at 0900 UTC predicted by the two groups of experiments (Figure9a,b), the temperature deviation predicted by the Control test in and around Beijing’s Fifth Ring Road (black rectangular frame) is larger than that by the MWRPS test. Especially, the Control test overestimates the observed heat island intensity in the urban area within the Fifth Ring Road. The assimilated data of the ground-based microwave radiometers corrected the warm bias in this area, and the modiﬁed indexes |BiasMWRPS| − |BiasControl| of 2 m air temperature are all negative, which is closer to the observation (Figure9c). Atmosphere 2021, 12, x FOR PEER REVIEW 12 of 19

Atmosphere 2021, 12, 551 Figure 8. Spatial distribution of observed air temperature at 2 m (unit: °C above the ground12 of of 18 0900 UTC on 4 May 2019. Note: the black rectangle box indicate the area (116.12° E–116.79° E, 39.65° N–40.115° N) in which temperature prior to the start of rainfall is statistically analyzed.

Figure 9. (a,b) The bias of 2 m temperature (unit: ◦C) simulated by Control experiment (Control minus Observation) and the Figure 9. (a,b) The bias of 2m temperature (unit: °C) simulated by Control experiment (Control minus Observation) and bias of 2 m temperature simulated by MWRPS experiment (MWRPS minus Observation) before belt-shaped echo splitting the bias of 2m temperature simulated◦ by MWRPS experiment (MWRPS minus Observation) before belt-shaped echo split- andting (c )and improving (c) improving index (unit:index C)(unit: after °C) assimilating after assimilating ground-based ground-based microwave microwave radiometer radiometer at 0900 UTC at 0900 on 4UTC May on 2019. 4 May 2019. The MWRPS test improves the prediction performance of 2 m temperature in Beijing, and better reproduces the observed weak heat island phenomenon. Thus, the observed The MWRPS test improves the prediction performance of 2 m temperature in Beijing, rainfall bifurcation can be simulated in the MWRPS experiment, due to the main effect and better reproduces the observed weak heat island phenomenon. Thus, the observed of urban surface in Beijing on rainfall through its dynamic action under low urban heat rainfall bifurcation can be simulated in the MWRPS experiment, due to the main effect of island conditions. urban surface in Beijing on rainfall through its dynamic action under low urban heat is- 4.land Discussion conditions. Furthermore, we compare the temperature difference in both the bottom layer and the 4. Discussion high layer from analysis fields of the two experiments initialized at 0600 UTC 4 May 2019. We findFurthermore, that negative we difference compare the in the temperature bottom layer difference and positive in both difference the bottom in thelayer high and layerthe high in Beijing layer wherefrom analysis the data fields of ground-based of the two experiments microwave initialized radiometers at 0600 are assimilated. UTC 4 May Compared2019. We withfind that the Controlnegative test, difference the MWRPS in th teste bottom has thermal layer and changes positive in low-level difference cooling in the andhigh high-level layer in Beijing heating where (Figure the 10 )data in its of analysis ground-based field. The microwave data assimilation radiometers of the are ground- assimi- basedlated. microwave Compared radiometers with the Control makes test, the atmosphericthe MWRPS stratificationtest has thermal of the changes initial fieldin low-level in this areacooling more and stable, high-level and the heating analysis (Figure field 10) lacks in theits analysis environment field. The support data toassimilation the emergence of the andground-based development microwave of convection, radiom whicheters is makes not conducive the atmospheric to the emergence stratification of precipitation. of the initial Thefield ground-based in this area more microwave stable, radiometer and the analys leadsis to field the coolinglacks the of environment the bottom layer support in Beijing to the inemergence the analysis and field development of the MWRPS of convection, test (Figure which 10). The is not system conducive modifies to the the initial emergence field in of time, thus affecting the prediction of air temperature.

Atmosphere 2021, 12, x FOR PEER REVIEW 13 of 19

Atmosphere 2021, 12, 551 13 of 18 precipitation. The ground-based microwave radiometer leads to the cooling of the bottom layer in Beijing in the analysis field of the MWRPS test (Figure 10). The system modifies the initial field in time, thus affecting the prediction of air temperature.

Figure 10. (Figurea) Temperature 10. (a) Temperature ﬁeld difference field difference between between MWRPS MW testRPS and test Control and Control analysis analysis (MWRPS (MWRPS minus Control, model level =minus 1) valid Control, from 0600 model UTC level on 4= May1) valid 2019 from (Beijing–Tianjin–Hebei 0600 UTC on 4 May region); 2019 (Beijing–Tianjin–Hebei (b) the same to (a), but re- for Level 27 of gion); (b) the same to (a), but for Level 27 of the model. the model.

Figure 11 showsFigure the 11radial shows vertical the radial profile vertical of temperature profile of temperatureand vertical velocity and vertical along velocity along ◦ 40° N in the boundary40 N in the layer boundary of the Fifth layer Ring of the Road Fifth and Ring its Road surrounding and its surrounding areas in the areas anal- in the analysis ysis field of twofield groups of two of groups experiments of experiments initialized initialized at 0600 UTC at 0600 on UTC4 May on 2019. 4 May It 2019.can be It can be seen seen that thethat heat the island heat simulated island simulated by the byControl the Control experiment experiment is too isstrong, too strong, producing producing strong strong updraftsupdrafts in the urban in the area. urban A area.clear updraft A clear updraftwas found was leading found to leading a rainfall to forecast a rainfall forecast in in Control experiment.Control experiment. This urban This ascending urban ascending motion is motion strengthened is strengthened under the under strong the strong heat island effect, which promotes the emergence of updrafts. The heat island effect simulated by heat island effect, which promotes the emergence of updrafts. The heat island effect sim- the MWRPS test assimilating the ground-based microwave radiation data in Beijing is weak. ulated by the MWRPS test assimilating the ground-based microwave radiation data in The sinking-motion-dominant boundary layer inhibits the emergence and development of Beijing is weak. The sinking-motion-dominant boundary layer inhibits the emergence and convection and accordingly affects the forecast of urban precipitation. The vertical velocity development of convection and accordingly affects the forecast of urban precipitation. The simulated by MWRPS experiment is negative and the ascending motion decreases, thus Atmosphere 2021, 12,vertical x FOR PEER velocity REVIEW simulated by MWRPS experiment is negative and the ascending motion 14 of 19 the simulated precipitation is reduced. It suggested that the ground-based microwave decreases, thus the simulated precipitation is reduced. It suggested that the ground-based radiometer observations provide more beneficial information for the initial conditions and microwave radiometer observations provide more beneficial information for the initial indirectly weaken the local updraft in the urban area in Beijing, consequently improve the conditions and indirectly weaken the local updraft in the urban area in Beijing, conse- rainfall bifurcation forecasts. quently improve the rainfall bifurcation forecasts.

FigureFigure 11. 11.Left Left and and right right figures figures are are cross cross sections sections of of the the vertical vertical profiles profiles along along 40 40°◦ N N fromfrom ControlControl andand MWRPS MWRPS analysis analysis field field initialized initialized atat 06000600 UTCUTC onon 44 MayMay 2019,2019, inin whichwhich thethe contourcontour representsrepresents the vertical velocity (unit: 10−1 m/s), and the shaded represents the temperature (unit: °C). the vertical velocity (unit: 10−1 m/s), and the shaded represents the temperature (unit: ◦C). Additionally, the spatial characteristics of 2 m temperature, 2 m specific humidity, and the 10 m wind speed distribution from forecasts was evaluated against observations through statistic metrics of mean bias and the root mean square error (RMSE). They are defined as:

n =−1 Bias() Fii O (1) n i=1

n =−1 2 RMSE() Fii O (2) n i=1

where Fi represents the value in the ith forecast gridded point and Qi is the corresponding observations. The closer the bias value is to 0 and the smaller the RMSE value is, the better the prediction is. Table 1 shows the bias and RMSE of the average temperature of 2 m, the specific humidity of 2 m and the wind speed of 10 m over the black rectangular boxes including urban and suburban areas in Beijing at 0900 UTC. It was predicted by two experiments initialized at 0600 UTC on 4 May 2019 prior to precipitation. Both experiments overestimate the observed 2m temperature, and the bias is positive. Both the bias and RMSE in the MWRPS test are smaller than those in the Control test. The 2m temperature forecast is closer to the actual observation, indicating better forecast performance. The 2m specific humidity is dry in the Control test, but wet in the MWRPS test which has slightly large RMSE. In light of 10m wind speed, the MWRPS test effectively improves the large wind speed forecast bias spotted in the Control test. To sum up, although in the MWRPS test the forecast error of 2 m specific humidity increases, the test addresses the warm bias of 2 m temperature and large bias of 10m wind speed, and improves the forecast performance of surface meteorological elements at 0900 UTC, thus laying a good foundation for the simulated urban precipitation-free process. The bias and RMSE of the 1-h accumulated precipitation are compared in Control and MWRPS experiment initialized at 0600 UTC over the urban and suburban areas in Beijing. It also shows the improvement in the forecast with the ground-based microwave radiometer data assimilation.

Table 1. Bias and RMSE comparison of the simulated and observed surface temperature, specific humidity, and wind speed.

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Additionally, the spatial characteristics of 2 m temperature, 2 m speciﬁc humidity, and the 10 m wind speed distribution from forecasts was evaluated against observations through statistic metrics of mean bias and the root mean square error (RMSE). They are deﬁned as: 1 n Bias = ∑ (Fi − Oi) (1) n i=1

s n 1 2 RMSE = ∑ (Fi − Oi) (2) n i=1

where Fi represents the value in the ith forecast gridded point and Qi is the corresponding observations. The closer the bias value is to 0 and the smaller the RMSE value is, the better the prediction is. Table1 shows the bias and RMSE of the average temperature of 2 m, the specific humidity of 2 m and the wind speed of 10 m over the black rectangular boxes including urban and suburban areas in Beijing at 0900 UTC. It was predicted by two experiments initialized at 0600 UTC on 4 May 2019 prior to precipitation. Both experiments overestimate the observed 2 m temperature, and the bias is positive. Both the bias and RMSE in the MWRPS test are smaller than those in the Control test. The 2 m temperature forecast is closer to the actual observation, indicating better forecast performance. The 2 m specific humidity is dry in the Control test, but wet in the MWRPS test which has slightly large RMSE. In light of 10 m wind speed, the MWRPS test effectively improves the large wind speed forecast bias spotted in the Control test. To sum up, although in the MWRPS test the forecast error of 2 m specific humidity increases, the test addresses the warm bias of 2 m temperature and large bias of 10 m wind speed, and improves the forecast performance of surface meteorological elements at 0900 UTC, thus laying a good foundation for the simulated urban precipitation-free process. The bias and RMSE of the 1-h accumulated precipitation are compared in Control and MWRPS experiment initialized at 0600 UTC over the urban and suburban areas in Beijing. It also shows the improvement in the forecast with the ground-based microwave radiometer data assimilation.

Table 1. Bias and RMSE comparison of the simulated and observed surface temperature, speciﬁc humidity, and wind speed.

2 m Speciﬁc Humidity Experiment 2 m Temperature(◦C) 10 m Wind Speed(m/s) (g/kg) Bias RMSE Bias RMSE Bias RMSE Control 1.65 1.67 −0.43 0.59 1.21 1.32 MWRPS 1.05 1.14 0.74 0.78 −0.32 0.59

Finally, the K-index simulated by the two experiments before the belt-shaped echo splitting is analyzed. It represents the atmospheric convection potential (warmth and humidity degree in the middle and lower layers and atmospheric stability) and has certain guiding signiﬁcance for the prediction. The K-index is deﬁned as:

K = (T850 − T500) + Td850 − (T700 − Td700) (3)

2 m Temperature 2 m Specific 10 m Wind Speed Experiment (°C) Humidity (g/kg) (m/s) Bias RMSE Bias RMSE Bias RMSE Control 1.65 1.67 −0.43 0.59 1.21 1.32 MWRPS 1.05 1.14 0.74 0.78 −0.32 0.59

Finally, the K-index simulated by the two experiments before the belt-shaped echo splitting is analyzed. It represents the atmospheric convection potential (warmth and humidity degree in the middle and lower layers and atmospheric stability) and has certain guiding significance for the prediction. The K-index is defined as:

KT=−+−−() T TdT ( Td ) 850500 850 700 700 (3)

where T represents temperature and Td represents dew point temperature, the value 850, 500, and 700 is the corresponding isobaric surfaces of 850 hPa, 500 hPa, and 700 hPa. In this formula, the first term represents the temperature drop rate, the second term rep- Atmosphere 2021, 12, 551 resents the water vapor condition in the lower layer, and the third term represents15 of the 18 saturation degree in the middle layer. Therefore, the K-index represents the thermal in- stability in the middle and low layers and the water vapor saturation in the layer of 700 hPa. The K-index can reflect the stratification stability of atmosphere [49]. TheThe largerlarger thethe K-indexK-index is,is, thethe moremore unstableunstable thethe stratificationstratification is.is. TheThe higherhigher thethe K-K- valuevalue is,is, thethe moremore likelylikely convectionconvection isis toto occuroccur [[50],50], andand thethe moremore unstableunstable thethe stratificationstratification ◦ is.is. Generally,Generally, thethe stratificationstratification isis ratherrather unstableunstable whenwhen thethe K-indexK-index isis higherhigher thanthan 3535 °C.C. ◦ FigureFigure 1212a,ba,b show show thatthat thethe areaarea withwith K-indexK-index greatergreater thanthan 3535 °CC inin BeijingBeijing inin thethe MWRPSMWRPS testtest isis smallersmaller thanthan thatthat inin thethe ControlControl test.test. ThisThis showsshows thatthat thethe structurestructure ofof thethe backgroundbackground fieldfield isis adjustedadjusted withwith thethe assimilatedassimilated datadata ofof thethe ground-basedground-based microwavemicrowave radiometers.radiometers. TheThe unstableunstable energy energy in in the the middle middle and and lower lower atmosphere atmosphere in in Beijing Beijing is reduced,is reduced, the the potential poten- oftial convection of convection occurrence occurrence and developmentand developmen is reduced,t is reduced, and the and occurrence the occurrence of precipitation of precipi- istation suppressed, is suppressed, so it can so it better can better predict predict the situation the situation of no of precipitation no precipitation in urban in urban area area at 1000at 1000 UTC UTC (Figure (Figure7). 7).

Figure 12. K-index at 0900 UTC on 4 May 2019 simulated by Control test at (a) and MWRPS test at (b) initialized at 0600 UTC beforeFigure belt-shaped 12. K-index echo at 0900 splitting. UTC on 4 May 2019 simulated by Control test at (a) and MWRPS test at (b) initialized at 0600 UTC before belt-shaped echo splitting. While the K-index in the Control test at 0900 UTC is stronger than that in the MWRPS test in Baoding, Hebei province where precipitation intensity is high at 1000 UTC. The While the K-index◦ in the Control test at 0900 UTC is stronger than that in the MWRPS coveragetest in Baoding, area above Hebei 35 provinceC is larger, where resulting precipitation in the precipitation intensity intensityis high at predicted 1000 UTC. by The the Controlcoverage test area at 1000above UTC 35 °C being is larger, one level resultin higherg in than the that precipitation observed (Figuresintensity7 andpredicted 12). For by thethe KControl index, test MWRPS at 1000 experiment UTC being outperforms one level higher the Control than that experiment observed before(Figures the 7 and start 12). of rainfall, it also has a better indication for forecasting the precipitation bifurcation. For the K index, MWRPS experiment outperforms the Control experiment before the start 5.of Conclusionsrainfall, it also has a better indication for forecasting the precipitation bifurcation. In view of the belt-shaped convection echo splitting process in Beijing on 4 May 2019, the RMASPS-ST was used to explore whether the data assimilation of the ground-based

microwave radiometers with high spatial and temporal resolution in Beijing could improve prediction. The two groups of experiments with and without the assimilation of MWRPS data were conducted in 3-h cycling runs. The simulation results for this rainfall bifurcation case obtained from the model system are verified. The experimental results show that the data assimilation of ground-based microwave radiometers in Beijing did improve the prediction of precipitation and echo, and better predicted the echo splitting process. The main conclusions are as follows: (1) Assimilating the ground-based microwave radiometer data can improve the initial field to a certain extent. In view of this process, the vertical structure configuration of temperature field and humidity field was improved, which plays an important role in correcting the forecast bias of the model, addressing the deficiency of the model to a certain extent. The RMAPS-ST model system can provide a good simulation of the selected rainfall case assimilating the MWRPS data in Beijing. It can prominently reproduce the observed urban heat island of the main urban area in Beijing prior to the start of this rainfall, thus reproducing the forecast of short-term cumulative precipitation bifurcation in the urban area. The simulated precipitation, radar reflec- Atmosphere 2021, 12, 551 16 of 18

tivity and surface temperature, specific humidity, and wind speed are all closer to the observation compared to the Control experiment. The urban effect on rainfall in Beijing cannot be neglected; (2) Different assimilation cycles have their corresponding improvement impacts on the initial field. The closer the assimilation cycle is, the more effective the precipitation forecast is. The MWRPS test valid from 0600 UTC has the best prediction effect, which is attributed to the thermodynamic condition under which the ground-based microwave radiometer data can be adjusted in time by cyclic assimilation. After the data of ground-based microwave radiometers are assimilated, the observed weak heat island phenomenon is better reproduced. The simulated surface temperature, specific humidity, and wind speed are also closer to the observation prior to the start of rainfall. The model not only prominently improves the forecast of precipitation distribution, but also makes the precipitation intensity prediction closer to the actual situation, and accurately predicts the process of belt-shaped echo splitting and precipitation bifurcation process in Beijing urban area; (3) The belt-shaped convection echo splitting process in Beijing on 4 May 2019 shows that the assimilation of ground-based microwave radiometer data can improve the numerical prediction of this process, and makes a positive contribution to improving the simulation of precipitation in this belt-shaped convection splitting process. It indicates that the assimilation of ground-based microwave radiometer data have a bright application prospect in numerical models. Assimilating MWRPS data is of great importance for numerical models, improving the quality of the initial conditions and the subsequent forecasts. This rainfall event can also help us understand the effect of urban surface on the rainfall system under weak urban heat island conditions. These conclusions are based on the rainfall bifurcation case occurred in the urban area of Beijing. Further investigation on the impact of assimilating ground-based microwave radiometer data on forecasts of the RMAPS-ST will continue. In addition, more sophisticated and advanced data assimilation method, such as the hybrid variational–ensemble approach and the data-driven machine learning method will hopefully further improve the effect of MWRPS data assimilation. However, conclusions from this study would be valuable for further understanding and application in regional numerical weather prediction models. Since the present study focused on only one case, the results may not be completely applicable to cases of other weather systems. More cases will be investigated to study the application of assimilation of ground-based microwave radiometer data in the future. In this way, we can have more insightful understanding of the impact of assimilation of ground-based microwave radiometer data on forecast.

Author Contributions: Conceptualization, B.L., Y.Q. and S.F.; methodology, Y.Q. and S.F.; software, Y.Q. and J.M.; validation, Y.Q.; formal analysis, Y.Q. and S.F.; computing resources, C.G. and S.Z.; writing—original draft preparation, Y.Q.; writing—review and editing, S.F., Y.Q. and C.G.; super- vision, S.F.; funding acquisition, S.F. and Y.Q. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Key Technologies Research and Development Program of China (2017YFC1501704), Key Laboratory for Cloud Physics of China Meteorological Administration LCP/CMA (2020Z007), National Natural Science Foundation of China (42005124), and the Key Open Experimental Project of Atmospheric Exploration of China Meteorological Admin- istration (KLAS201701). Data Availability Statement: The ground-based microwave radiometers data can be available from Meteorological Observation Center of China Meteorological Administration. Acknowledgments: We thank RMAPS-ST Authority for code support and sharing valuable data. We acknowledge Meteorological Observation Center of China Meteorological Administration for the ground-based microwave radiometers data. Conﬂicts of Interest: The authors declare no conﬂict of interest. Atmosphere 2021, 12, 551 17 of 18

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