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Contrasting the X-band phased array radar (XPAR) with the conventional S-Band dual-polarization mechanical scanning radar (SMSR), the XPAR offers superior temporal and spatial resolution, enabling a more refined depiction of the internal dynamics within convective systems. While both SMSR and XPAR data are extensively used in monitoring and alerting for severe convective weather, their comparative application in numerical weather prediction through data assimilation remains a relatively unexplored area. This study harnesses the Weather Research and Forecasting Model (WRF) and its data assimilation system (WRFDA) to integrate radial velocity and reflectivity from the Guangzhou SMSR and nine XPARs across Guangdong Province. Utilizing a three-dimensional variational approach at a 1 km convective-scale grid, the assimilated data are applied to forecast a rainstorm event in the Pearl River Delta (PRD) on 6 June 2022. Through a comparative analysis of the results from assimilating SMSR and XPAR data, it was observed that the assimilation of SMSR data led to more extensive adjustments in the lower- and middle-level wind fields compared to XPAR data assimilation. This resulted in an enlarged convergence area at lower levels, prompting an overdevelopment of convective systems and an excessive concentration of internal hydrometeor particles, which in turn led to spurious precipitation forecasts. However, the sequential assimilation of both SMSR and XPAR data effectively reduced the excessive adjustments in the wind fields that were evident when only SMSR data were used. This approach diminished the generation of false echoes and enhanced the precision of quantitative precipitation forecasts. Additionally, the lower spectral width of XPAR data indicates its superior detection accuracy. Assimilating XPAR data alone yields more reasonable adjustments to the low- to middle-level wind fields, leading to the formation of small-to-medium-scale horizontal convergence lines in the lower levels of the analysis field. This enhancement significantly improves the model's forecasts of composite reflectivity and radar echoes, aligning them more closely with actual observations. Consequently, the Threat Score (TS) and Equitable Threat Score (ETS) for heavy-rain forecasts (>10 mm/h) over the next 5 h are markedly enhanced. This study underscores the necessity of incorporating XPAR data assimilation in numerical weather prediction practices and lays the groundwork for the future joint assimilation of SMSR and XPAR data. [ABSTRACT FROM AUTHOR]

The capabilities of assimilating the all‐sky Fengyun‐4A Advanced Geostationary Radiation Imager (AGRI) infrared radiances (IR) are completed by including hydrometers in the observation operator, its adjoint, and tangent linear model. This allows the three‐dimensional variational data assimilation model to include cloud‐precipitation information from infrared IR observations. Advanced as the all‐sky data assimilation methodologies are, the assimilation of cloudy scene IR radiances for tropical cyclone (TC) systems has not led to consistently better results, especially for the intensity of TCs. This work explores the effects of all‐sky AGRI radiance assimilation on a Super Typhoon In‐Fa (2106) during its stage experiencing abnormal changes in the intensity and the track. It is shown that the all‐sky assimilation of AGRI two channels 9–10 brings no obviously better TC forecasts than the all‐sky AGRI single‐channel assimilation does. Besides, the O − B (observation minus background) bias was corrected to be even larger with the variational bias correction method for the pixels with relatively lower or higher cloud impact. This indicates that traditional bias correction schemes with linear fitting functions are suboptimal if the relations between the predictor and O − B biases are non‐linear. When the conventional observation and IR radiances are assimilated in two steps, the wind in the inner‐core region is better described to properly capture the changes in the typhoon intensity. Generally, the analyses and forecasts of Typhoon In‐Fa from experiments with the all‐sky IR observations are enhanced compared to those with only the clear‐sky IR observations. Plain Language Summary: It is a challenge to accurately capture the variability of Tropical cyclones' (TCs) intensity and track in numerical weather predictions. The assimilation of infrared radiances under all‐sky conditions is able to efficiently utilize the cloud‐related observations over the key areas of TC. In the study, possible factors are discussed that may affect the efficacy of the all‐sky assimilation of Fengyun‐4A (FY‐4A) Advanced Geostationary Radiation Imager (AGRI) observations for Typhoon In‐Fa during the period with abnormal changes in the track and intensity. It is found that assimilating the AGRI two channels 9–10 brings no significantly better forecasts than the AGRI single‐channel assimilation does under all‐sky conditions. The systematic bias were corrected to be even larger with the variational bias correction method for the pixels in cloud scenes. When the conventional observations and AGRI radiances are assimilated in two steps, the TC wind structure is better described to capture the changes in the typhoon intensity. Meanwhile, the superiority of all‐sky data assimilation is confirmed regarding the intensity and track forecasts of Typhoon In‐Fa. Key Points: Assimilating the all‐sky infrared radiances (IR) is completed by including hydrometers in the observation operator, its tangent linear, and adjoint modelSeveral possible factors are investigated and discussed that affect the efficacy of all‐sky assimilation of Advanced Geostationary Radiation Imager (AGRI) observationsTyphoon intensity and track forecasts are improved by assimilating FY‐4A AGRI all‐sky IR [ABSTRACT FROM AUTHOR]

Probability forecast is an objective product derived from ensemble forecasts and contains information of uncertainty, which has important reference value for operational decision-making services. In the traditional neighborhood ensemble probability method (NEP), the neighborhood radius is always a constant and does not conform to the scale spectrum involved in the practical weather events. Therefore, the Neighborhood Ensemble Probability based on Ensemble Agreement Scale (EAS_NEP) was introduced, and comprehensive evaluations were conducted for two Meiyu frontal heavy rainfall events in southern China, in order to verify the applicability of this method in such events and promote its popularization in practical operation. The ensemble forecasts obtained by combining the initial condition, lateral boundary and physical process of perturbations can better represent the practical prediction uncertainty. Based on this, the Grid-to-grid Probability (GP), NEP with different radii and EAS_NEP were compared for accuracy, forecast skill and other aspects of performance. The results showed that EAS_NEP can adjust the neighborhood radius adaptively according to the agreement between ensemble members and its radii tend to be lager for concentrated precipitation than dispersed precipitation. These dynamically adjusted radii not only avoid excessive smoothing and key information loss when the neighborhood radius is too large, but also eliminate the singular points caused by small radius and possess stepped spatial distribution with better continuity. In addition, quantitative evaluation results such as BS, FSS and ROC curves also confirm that EAS_NEP has improved forecasting skills compared to traditional methods, especially in the case of dispersed precipitation and high threshold situation. The results suggest that EAS_NEP has a good application prospect in the Meiyu frontal heavy rainfall events and can effectively improve the quality of probability forecast. [ABSTRACT FROM AUTHOR]

The all‐sky assimilation module for Advanced Geostationary Radiation Imager (AGRI) onboard the Chinese new generation of geostationary meteorological satellites Fengyun‐4A (FY‐4A) is constructed for the first time in Weather Research and Forecasting Model Data Assimilation (WRFDA) model with Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV). Based on the characteristics of bias distribution, the cloud effect average is selected as a new bias correction (BC) predictor to remove cloud‐related biases in all‐sky conditions. The impact of the modified BC scheme and AGRI all‐sky assimilation on typhoon Lekima (2019) forecast is evaluated through a set of cycle assimilation experiments with WRFDA 3DVAR component. The result shows that, compared with other BC schemes, the modified BC scheme can effectively reduce the mean standard deviation, root mean square error, and absolute mean of observation departure in all‐sky conditions. Meanwhile, the AGRI all‐sky assimilation with the modified BC scheme obtains significant improvements in track prediction. Substantial error reductions in model variables, such as wind, temperature, and humidity, are also produced. Plain Language Summary: The Advanced Geostationary Radiation Imager (AGRI) onboard the Chinese new generation of geostationary meteorological satellites Fengyun‐4A (FY‐4A) has many advantages. However, current research only uses its clear‐sky radiances data to improve atmosphere initializations, ignoring many critical weather systems covered by cloud. In this study, we establish an all‐sky assimilation module for AGRI radiances data and select a new bias correction (BC) predictor to remove cloud‐related biases in all‐sky conditions. The evaluation shows that the AGRI all‐sky assimilation with the modified BC scheme can mitigate observation departure in all‐sky conditions. Meanwhile, it can improve the track prediction of typhoon Lekima (2019) and reduce the forecast errors of model variables, including wind, temperature, and humidity. Key Points: It is the first attempt to assimilate all‐sky Advanced Geostationary Radiation Imager (AGRI) radiance data using WRFDA system with RTTOV cloudy radiance simulation moduleFor all‐sky AGRI radiance data, the cloud effect average used as a new bias correction predictor effectively reduces systematic deviationsThe AGRI all‐sky assimilation with a new bias correction predictor significantly improves Lekima's track and environment variable forecasts [ABSTRACT FROM AUTHOR]

Ground-based microwave radiometer (GMWR) data with high spatial and temporal resolution can improve the accuracy of weather forecasts when effectively assimilated into numerical weather prediction. Nowadays, the major method to assimilate these data is via indirect assimilation by assimilating the retrieved profiles, which introduces large retrieval errors and cannot easily be represented by an error covariance matrix. Direct assimilation, on the other hand, can avoid this issue. In this study, the ground-based version of the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-gb) was selected as the observation operator, and a direct assimilation module for GMWR radiance data was established in the Weather Research and Forecasting Model Data Assimilation (WRFDA). Then, this direct assimilation module was applied to assimilate GMWR data. The results were compared to the indirect assimilation experiment and demonstrated that direct assimilation can more effectively improve the model's initial fields in terms of temperature and humidity than indirect assimilation while avoiding the influence of retrieval errors. In addition, direct assimilation performed better in the precipitation forecast than indirect assimilation, making the main precipitation center closer to the observation. In particular, the improvement in the precipitation forecast with a threshold of 60 mm/6 h was obvious, and the corresponding TS score was significantly enhanced. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting refers to the use of specific meteorological elements to predict precipitation in the next 0–2 h. Existing methods use radar echo maps and the Z–R relationship to directly predict future rainfall rates through deep learning methods, which are not physically constrained, but suffer from severe loss of predicted image details. This paper proposes a new model framework to effectively solve this problem, namely LSTMAtU-Net. It is based on the U-Net architecture, equipped with a Convolutional LSTM (ConvLSTM) unit with the vertical flow direction and depthwise-separable convolution, and we propose a new component, the Efficient Channel and Space Attention (ECSA) module. The ConvLSTM unit with the vertical flow direction memorizes temporal changes by extracting features from different levels of the convolutional layers, while the ECSA module innovatively integrates different structural information of each layer of U-Net into the channelwise attention mechanism to learn channel and spatial information, thereby enhancing attention to the details of precipitation images. The experimental results showed that the performance of the model on the test dataset was better than other examined models and improved the accuracy of medium- and high-intensity precipitation nowcasting. [ABSTRACT FROM AUTHOR]

Tornadoes are extremely destructive natural disasters, and East China has become a high-incidence area for tornadoes in China in recent years. On 7 July 2013, an EF2-intensity tornado occurred in Gaoyou County, Jiangsu Province in eastern China, within a supercell storm near a Meiyu frontal system. To investigate the dynamical process of the tornado, a numerical simulation was performed using four one-way nested grids within the Advanced Regional Prediction System (ARPS). Data from a nearby operational S-band Doppler radar are assimilated using a 4D ensemble Kalman filter (4DEnKF) at 5 min intervals. Forecasts are run with a nested 50 m grid, capturing the tornado embedded within the supercell storm with a reasonable agreement with observations. The tornadogenesis processes within the simulation results are analyzed in detail, including the three-dimensional evolution of the tornado vortex. It is found that a cold surge within the rear flank downdraft region plays a key role in instigating tornadogenesis when the leading edge of the cold surge approaches a near-ground convergence center located underneath the main updraft, and the enhancement of the convergence center caused by the descending of the low-level mesocyclone is the direct cause of the rapid increase in tornado vorticity. Backward trajectories are calculated based on model output, and the origins of the parcels feeding the intensifying tornado vortex are identified. It is found that parcels from the mid-level of the rear flank downdraft region follow the cold surge, descending to the ground under the influence of the downdraft in the cold surge, and then entering the convergence center, merging into the core of the tornado and being lifted up. Vertical profiles of the mass and vorticity fluxes into the core of the tornado vortex are examined, and it is found that the near-ground airflow contributes significantly to the growth of the tornado vorticity, with the contribution increasing as it gets closer to the ground. [ABSTRACT FROM AUTHOR]

In order to explore the improvement of radar radial velocity data assimilation on the initial and forecast fields of typhoons, this study assimilates the quality-controlled radial velocity data in the case of Typhoon Linfa (2015) using the three-dimensional variational data assimilation system of the weather research and forecasting model (WRF-3DVAR), and then conducts several sensitivity experiments with different horizontal length scale factors. The results show that reducing the horizontal length scale factor of the background error covariance can effectively assimilate the micro- and meso-scale information from radar data and improve the forecasting effect of Linfa. Following the optimization of the horizontal length scale factor, the radial velocity data assimilation can improve the typhoon wind field structure, produce reasonable cyclonic wind field increments, and further improve the dynamic and thermal structure of the inner core area of the typhoon. Then, we can obtain a better initial field of model forecasting, and thus typhoon track and intensity forecasting are improved. [ABSTRACT FROM AUTHOR]

In this study, all-sky GOES-R ABI infrared radiances at their native resolution are assimilated using an enhanced GSI ensemble Kalman filter (EnKF) data assimilation (DA) system, and the impacts of the data on the analysis and forecast of a mesoscale convective system (MCS) are explored. Results show that all-sky ABI BT data can correctly build up observed storms within the model and effectively remove spurious storms in model background through frequent DA cycles. Both bias and root-mean-squared innovation of the background and analysis are significantly reduced during the DA cycles, and free forecasts are improved when verified subjectively and objectively against observed ABI BTs and independent radar reflectivity observations. A horizontal localization radius of 30 km is found to produce the best results while 5-min DA cycles improve the storm analyses over 15-min cycles, but the differences in forecasts are small. Further analyses show that the clearing of spurious clouds by ABI radiance is correctly accompanied by reduction in moisture through background error cross covariance, but overdrying often occurs, which can cause spurious storm decay in the forecast. The problem is reduced when the ensemble mean of observation prior instead of observation prior of the ensemble mean state is used in the ensemble mean state update equation of EnKF. The significant difference between the two ways that the ensemble mean of observation prior is calculated when the observational operator is very nonlinear has not been recognized in earlier cloudy radiance DA studies. Significance Statement: Satellite observations in cloudy regions are not used in most current operational weather prediction systems due to complex nonlinear relations between satellite-observed quantities, the radiances, and model state in such regions. The models also must predict clouds reasonably well for cloudy observations to be effectively assimilated. The latest NOAA geostationary satellites can provide radiance observations at high spatial and temporal resolutions and such data in both cloudy- and clear-air regions are assimilated using an advanced data assimilation method into a model that explicitly represents convection. Forecasts up to 4 h are improved by the assimilation while several issues associated with the assimilation are discussed. The study contributes to the eventual use of all-sky satellite radiance data in operational models. [ABSTRACT FROM AUTHOR]

The application of lateral boundary perturbations (LBPs) helps to restore dispersion in convection-allowing ensemble forecasts (CAEFs). However, the applicability of LBPs remains unclear because of the differences between convection systems. Short-range (24 h) ensemble forecasts are carried out to explore this issue with a strong-forcing (SF) case and a weak-forcing (WF) case in East China. The dependence of LBPs on the forcing types of severe convection is investigated regarding the forecast error growth caused by the lateral boundary conditions (LBCs). The results show that the LBPs mainly influence the SF case rather than the WF case, especially after a 12-h forecast. The large-scale errors dominate in the SF case because the change in the synoptic-scale system affects the forecast error evolution. In contrast, the large-scale errors are mainly derived from the upscaling of the small-scale errors in the WF case, indicating that using LBPs is only insufficient in such a case. In sensitivity experiments that vary the magnitude of LBPs from 10% to 150% of its original value, CAEFs demonstrate more sensitive to LBPs in the SF case than in the WF case, indicating that the WF case has intrinsically limited predictability. Overall, LBPs are more suitable for the SF case, while additional perturbations from other sources are required for CAEFs in the WF case because of the limits of intrinsic predictability. [ABSTRACT FROM AUTHOR]

The study is based on the Weather Research and Forecasting Model (WRF) and the three-dimensional variational (3DVAR) data assimilation system. Based on two different control variables, the effects of radar radial velocity assimilation in forecasting of the tropical cyclone (TC) Kompasu were evaluated. The single observation experiment showed that DA_ψχ produces cyclonic increments, while DA_UV only produces increments in the same direction as the observation. DA_ψχ significantly enhances the wind field at 850 hPa with a large number of unphysical cyclonic increments. On the other hand, DA_UV produces reasonable cyclonic increments to enhance the TC. The assimilation of DA_UV makes the surface wind enhanced and the sea level pressure at the TC center reduced. The circular structure of the DA_ψχ wind field is not clear and neither is the large wind area concentrated. In addition, the DA_ψχ shows spurious convection at the high altitude of the vertical cross section, while the DA_UV presents enhanced large wind area at the bottom. The RMSE of the radial velocity is smaller during the circular assimilation in DA_UV. DA_ψχ does not improve the track forecast of Kompasu, while DA_UV shows a significant improvement by the track forecast. [ABSTRACT FROM AUTHOR]

Reliable quantitative precipitation forecasting is essential to society. At present, quantitative precipitation forecasting based on weather radar represents an urgently needed, yet rather challenging. However, because the Z-R relation between radar and rainfall has several parameters in different areas, and because rainfall varies with seasons, traditional methods cannot capture high-resolution spatiotemporal features. Therefore, we propose an attention fusion spatiotemporal residual network (AF-SRNet) to forecast rainfall precisely for the weak continuity of convective precipitation. Specifically, the spatiotemporal residual network is designed to extract the deep spatiotemporal features of radar echo and precipitation data. Then, we combine the radar echo feature and precipitation feature as the input of the decoder through the attention fusion block; after that, the decoder forecasts the rainfall for the next two hours. We train and evaluate our approaches on the historical data from the Jiangsu Meteorological Observatory. The experimental results show that AF-SRNet can effectively utilize multiple inputs and provides more precise nowcasting of convective precipitation. [ABSTRACT FROM AUTHOR]

In the present work, we investigate the impacts on the observation error (OE) statistics due to different types of errors in the forward operator (FE) for both radar reflectivity and radial wind data, in the context of convective-scale data assimilation in the summertime. A series of sensitivity experiments were conducted with the Efficient Modular VOlume RADar Operator (EMVORADO), using the operational data assimilation system of the Deutscher Wetterdienst (DWD, German Weather Service). The investigated FEs are versatile, including errors caused by neglecting the terminal fall speed of hydrometeor, the reflectivity weighting, and the beam broadening and attenuation effects, as well as errors caused by different scattering schemes and formulations for melting particles. For reflectivity, it is found that accounting for the beam broadening effect evidently reduces the standard deviations, especially at higher altitudes. However, it does not shorten the horizontal or along-beam correlation length scales. In comparison between the Rayleigh and the Mie schemes (with specific configurations), the former one results in much smaller standard deviations for heights up to 4 km, and aloft, slightly larger standard deviations. Imposing the attenuation to the Mie scheme slightly reduces the standard deviations at lower altitudes; however, it largely increases the standard deviations at higher altitudes and it also leads to longer correlation length scales. For radial wind, positive impacts of considering the beam broadening effect on standard deviations and neutral impacts on correlations are observed. For both reflectivity and radial wind, taking the terminal fall speed of hydrometeor and the reflectivity weighting into account does not make remarkable differences in the estimated OE statistics. [ABSTRACT FROM AUTHOR]

We explore impacts of model domain size on rainfall forecasts over Zhejiang Province during the Meiyu season in this paper. The existing modeling system is based on the Weather Research and Forecasting (WRF), which is referred to as the Zhejiang WRF with Advanced Data Assimilation System (ADAS) Real-time Modeling System (ZJWARMS). First, we identify the regions with large uncertainties of model forecasts using correlation analysis of different reanalysis datasets. Five key regions affecting precipitation in Zhejiang are identified by calculating the correlation-coefficients between precipitation in Zhejiang and large-scale atmospheric factors. A new WRF model domain, called ZJdomain, is then chosen to cover the key regions and to avoid the regions with large uncertainties. To evaluate the differences of precipitation forecasts in each precipitation category between using the new domain, ZJdomain, and using the current operational model domain, ZJWARMS, two sets of experiments are conducted, including two case studies and a batch test based on daily 72-h forecasts. The results suggest that the ZJdomain can better simulate the key weather systems and their associated precipitation in Zhejiang. Quantitative tests of different-category precipitation also show that the equivalent threat score (ETS) increases significantly, and the frequency of missed (FOM) and the false alarm ratio (FAR) decrease with varying degrees. In the batch test, the ETSs of the torrential rain in forecasts with the lead times of 24–48 h and 48–72 h increase by 100.3% and 62.1%, respectively. Compared with the Shanghai Meteorological Service WRF ADAS Real-time Modeling System version 2.0 (SMSWARMS), it is found that the unsuitable WRF model domain is one of the main reasons that the precipitation forecast error of ZJWARMS is always larger than that of SMSWARMS. This study demonstrates that the selection of a suitable model domain is a key to improve the forecast of regional operational numerical weather prediction systems. [ABSTRACT FROM AUTHOR]

This study developed a hybrid four-dimensional (4D) ensemble–variational (4DEnVar) radar data assimilation (DA) system for the Weather Research and Forecasting model. The 4DEnVar approach incorporated ensemble covariances at multiple time levels to assimilate observations distributed in the assimilation windows. By approximating the evolution of the background error using 4D ensemble covariance, use of the tangent linear and adjoint models was avoided. The impact of 4DEnVar radar DA on convective-scale analyses and forecasts was examined through comparison with 3DVar and 3DEnVar methods for the case of a squall line that occurred over southeastern China. In comparison with the other methods, 4DEnVar produced both smaller root mean square innovations for radar reflectivity and radial velocity and better analysis of the vertical structure of reflectivity. The corresponding relative humidity and vertical wind in convective regions were strengthened. Ultimately, 4DEnVar produced a substantially improved forecast, including improved quantitative precipitation and reflectivity forecast skill, and better representation of the squall line in terms of both areal coverage and intensity. In contrast, 3DEnVar improved the analysis and forecast modestly in comparison with 3DVar. Furthermore, sensitivity experiments indicated that a moderate assimilation window and a stronger ensemble weighting factor used in 4DEnVar could produce superior forecast results. The wind, temperature and water vapor were also improved by 4DEnVar, with the largest bias reduction for water vapor at low and middle levels. The improvements of 4DEnVar were further verified and shown effective using a mesocale convective system case and a local convection case. [ABSTRACT FROM AUTHOR]

For convective clouds and precipitation, model uncertainty in cloud microphysics is considered one of the most significant sources of model error. In this study, samples for model microphysical uncertainty are obtained by calculating the differences between simulations equipped with two‐ and one‐moment schemes during a one‐month training period. The samples are then added to convective‐scale ensemble data assimilation as additive noise and combined with large‐scale additive noise based on samples from climatological atmospheric background error covariance. Two experiments, including the combination and large‐scale error only, are conducted for a one‐week convective period. The results reveal that the simulation with a two‐moment scheme triggers more convection and has larger ice‐phase precipitation particles, which produce a stronger signal in the melting layer. During data assimilation cycling, although more water is introduced to the model, it is shown that the combination performs better for both background and analysis and significantly improves short‐term ensemble forecasts of radar reflectivity and hourly precipitation. Plain Language Summary: One of the main difficulties hindering the improvements of weather forecasts is correct representation of uncertainties in clouds and precipitation in the numerical weather prediction models. The goal of this work is to improve the representation of this uncertainty when combining our prediction with observations. This way, we would obtain better initial condition for our model and better prediction of convection. Here, we obtain the samples for model error by computing the differences between simulations that use two cloud microphysical schemes in the model. Then such obtained samples for model microphysical uncertainty are combined with large‐scale error and incorporated into data assimilation. To evaluate the performance of this method, two experiments are carried out in a one‐week period over Germany. We find that the combination indeed achieves significant improvement of short‐term forecasts of radar reflectivity and hourly precipitation. Key Points: Model simulations with two microphysical schemes are used to represent uncertainty in clouds and precipitation during data assimilationIn data assimilation, including the combination of microphysical and large‐scale uncertainty improves the performanceThe improvement is also significant for short‐term ensemble forecasts of radar reflectivity and hourly precipitation [ABSTRACT FROM AUTHOR]

Assimilation of weather radar measurements including radar reflectivity and radial wind data has been operational at the Deutscher Wetterdienst, with a diagonal observation error (OE) covariance matrix. For an implementation of a full OE covariance matrix, the statistics of the OE have to be a priori estimated, for which the Desroziers method has been often used. However, the resulted statistics consists of contributions from different error sources and are difficult to interpret. In this work, we use an approach that is based on samples for truncation error in radar observation space to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with the OE statistics estimated by the Desroziers method. It is found that the statistics of the RE help the understanding of several important features in the variances and correlation length scales of the OE for both reflectivity and radial wind data and the other error sources from the microphysical scheme, radar observation operator and the superobbing technique may also contribute, for instance, to differences among different elevations and observation types. The statistics presented here can serve as a guideline for selecting which observations are assimilated and for assignment of the OE covariance matrix that can be diagonal or full and correlated. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Himawari-8 is a next-generation geostationary meteorological satellite launched by the Japan Meteorological Agency. It carries the Advanced Himawari Imager (AHI) on board, which can continuously monitor high-impact weather events with high frequency in space and time. The assimilation of AHI radiance data was implemented with the three-dimensional variational data assimilation system (3DVAR) of the Weather Research and Forecasting Model for the analysis and prediction of Typhoon Soudelor (2015) in the Pacific typhoon season. The effective assimilation of AHI radiance data in improving the forecast of the tropical cyclone during its rapid intensification has been realized. The results show that, after assimilating the AHI radiance data under clear-sky conditions, the typhoon position in the background field of the model was effectively corrected compared with the control experiment without AHI radiance data assimilation. It is found that the assimilation of AHI radiance data is able to improve the analyses of the water vapor and wind in a typhoon's inner-core region. The analyses and forecasts of the minimum sea level pressure, the maximum surface wind, and the track of the typhoon are further improved. [ABSTRACT FROM AUTHOR]

Error growth is investigated based on convection-allowing ensemble forecasts starting from 0000 UTC for 13 active convection events over central to eastern U.S. regions from spring 2018. The analysis domain is divided into the northwest (NW), northeast (NE), southeast (SE), and southwest (SW) quadrants (subregions). Total difference energy and its decompositions are used to measure and analyze error growth at and across scales. Special attention is paid to the dominant types of convection with respect to their forcing mechanisms in the four subregions and the associated difference in precipitation diurnal cycles. The discussions on the average behaviors of error growth in each region are supplemented by four representative cases. Results show that the meso-γ-scale error growth is directly linked to precipitation diurnal cycle while meso-α-scale error growth has a strong link to large-scale forcing. Upscale error growth is evident in all regions/cases but up-amplitude growth within its own scale plays different roles in different regions/cases. When large-scale flow is important (as in the NE region), precipitation is strongly modulated by the large-scale forcing and becomes more organized with time, and upscale transfer of forecast error is stronger. On the other hand, when local instability plays more dominant roles (as in the SE region), precipitation is overall less organized and has the weakest diurnal variations. Its associated errors at the γ and β scale can reach their peaks sooner and meso-α-scale error tends to rely more on growth of error with its own scale. Small-scale forecast errors are directly impacted by convective activities and have a short response time to convection while increasingly larger-scale errors have longer response times and delayed phase within the diurnal cycle. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The impact of stochastically perturbing the terminal velocities of hydrometeors on convective-scale ensemble forecasts of precipitation was examined. An idealized supercell storm case was first used to determine the terminal velocity error characteristics for a one-moment microphysics scheme in terms of the terminal velocities from a two-moment scheme. Two real cases were employed to evaluate the forecast skills resulting from perturbing the terminal velocities with real data. The results indicated that the one-moment scheme produced terminal velocities that were approximately three times higher than those of the two-moment scheme for snow and hail, which often resulted in overpredictions of hourly precipitation and areal accumulated precipitation. Therefore, stochastically perturbing the terminal velocities according to their error characteristics matched the observed hourly precipitation and areal accumulated precipitation better than the symmetrical perturbations. For the two-moment scheme, the symmetrical perturbations of the terminal velocities tended to produce lower falling speeds of precipitation hydrometeors; therefore, more light rain was produced. Compared to the unperturbed two-moment scheme, symmetrically perturbing the terminal velocities resulted in smaller precipitation errors when precipitation was overestimated but comparable or slightly larger precipitation errors when precipitation was underestimated. This work demonstrates the sensitivity of precipitation ensemble forecasts to terminal velocity perturbations and the potential benefits of adopting these perturbations; however, whether the perturbations actually result in significant improvements in precipitation forecast skill needs further study. [ABSTRACT FROM AUTHOR]

Warm‐sector torrential rainfall (WR) in the South China monsoon region has long been a forecasting challenge because of the limited capability of numerical models in heavy rainfall without strong synoptic forcing. Through convection‐allowing ensemble forecasts, this study explores both the intrinsic and practical predictability of a coastal WR event on 19–20 May 2015 during SCMREX (the Southern China Monsoon Rainfall Experiment). The results show a large variability in forecast performance among different members, indicating the practical limit of predictability. In general, GOOD members tend to have a stronger low‐level southerly wind over the sea (monsoon flow) and a considerable surface cooling over the northern mountains (associated with land/mountain breeze). Further investigation via ensemble‐based sensitivity analysis shows that the occurrence of WR is closely related to the nighttime strengthened (cooling) southerly wind (temperatures) over the sea (mountains), 1–3 hr prior to the convection initiation. In contrast, spatial scaling of the initial perturbations has little impact on the forecast after 3 hr and the meso‐γ‐scale rainfall is fully decorrelated after 12 hr, suggesting an intrinsic predictability limit for lead times as short as 6–12 hr. Sensitivity experiments are conducted with the initial‐condition differences reduced by almost an order of magnitude smaller than typical ensemble perturbations, with the results demonstrating that the rainstorm might be near the point of bifurcation, where predictability is intrinsically limited. The limits of both intrinsic and practical predictability highlight the need for rapidly updated and probabilistic convection‐allowing ensemble forecasts for events of this type. Key Points: Both the practical and intrinsic predictability of a coastal warm sector torrential rainfall event during the Southern China Monsoon Rainfall Experiment (SCMREX) are investegatedThe occurrence of warm‐sector rainfall is closely related to the nighttime strengthened (cooling) monsoon flow (temperature) over the sea (mountain)The warm‐sector rainstorm could be near the point of bifurcation, where predictability is intrinsically limited [ABSTRACT FROM AUTHOR]

To obtain better performance of meteorological applications, it is necessary to distinguish radar echoes from meteorological and non-meteorological targets. After a comprehensive analysis of the computational efficiency and radar system characteristics, we propose a fuzzy logic method that is similar to the MetSignal algorithm; the performance of this method is improved significantly in weak-signal regions where polarimetric variables are severely affected by noise. In addition, post-processing is adjusted to prevent anomalous propagation at a far range from being misclassified as meteorological echo. Moreover, an additional fuzzy logic echo classifier is incorporated into post-processing to suppress misclassification in the melting layer. An independent test set is selected to evaluate algorithm performance, and the statistical results show an improvement in the algorithm performance, especially with respect to the classification of meteorological echoes in weak-signal regions. [ABSTRACT FROM AUTHOR]

The background error covariance is an important component in data assimilation systems, which largely dominates the error correlations between different analysis variables. This study aimed to assess the impact of the determination of the background error length and variance scale factors on the track and intensity forecast of hurricane systems for radar radial velocity (Vr) data assimilation. To test the sensitivity of the background error length scale and variance scale, multiple data assimilation analyses were performed using a 3D variational data assimilation method with a parameter‐sweeping strategy for the case of Hurricane Ike (2008) by varying the decorrelation length scale (LEN_SCALING) and the variance (VAR_SCALING) of the background error step by step. It is shown that, in radar Vr data assimilation, generally smaller variance scale and length scale factors tend to improve the prediction of the hurricane track. Setting both VAR_SCALING and LEN_SCALING equivalent to 0.4 provides a better track than other combinations. In particular, the impact of the variance scale factor on the track forecasts is larger than that of the length scale factor. There is no direct impact of tuning variance scales and length scales on the forecast intensity for the first few forecast hours. Tuning length scale factors less than 0.4 provides smaller forecast errors of minimum sea level pressure and maximum surface wind with increasing forecast lead time. [ABSTRACT FROM AUTHOR]

A spatially and temporally varying Bayesian inflation algorithm was applied to the assimilation of the real radar data of a mesoscale convective system (MCS), which occurred on May 8–9, 2007 in Oklahoma and Texas (USA), using the ensemble square root filter (EnSRF) within the advanced regional prediction system (ARPS). For comparison purposes, two radar data assimilation and forecast experiments using Bayesian inflation and regular multiplicative inflation algorithms were performed. Results indicated that the analysis reflectivity structure of the MCS using the Bayesian inflation method appeared better than when using the multiplicative inflation method. The Bayesian inflation method also reduced the root mean square innovations (RMSIs) and increased the ensemble spreads for reflectivity and radial velocity. Furthermore, the reduction of RMSIs was related to the higher Bayesian inflation parameter assigned to the background. The flow‐dependent characteristic of the Bayesian inflation parameter means it varies with space and model state variables. This successful analysis helped improve subsequent forecast reflectivity and precipitation, especially for stratiform precipitation. It also promoted the development of the MCS with a strong surface cold pool and outflow in the convective regions. Quantitative reflectivity and precipitation forecast skills were also improved in the Bayesian inflation experiment. These encouraging results demonstrated the greater potential of using the Bayesian inflation algorithm for EnSRF radar data assimilation compared with multiplicative inflation. [ABSTRACT FROM AUTHOR]

To improve severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize NWP forecasts at convection-resolving scales. The first step of the algorithm identifies areas of deep moist convection by utilizing the vertically integrated liquid water (VIL) derived from three-dimensional radar reflectivity fields. Once VIL is obtained, pseudo–water vapor observations are derived based on reflectivity thresholds within columns characterized by deep moist convection. Areas of spurious convection also are identified by the algorithm to help reduce their detrimental impact on the forecast. The third step is to assimilate the derived pseudo–water vapor observations into a convection-resolving-scale NWP model along with radar radial velocity and reflectivity fields in a 3DVAR framework during 4-h data assimilation cycles. Finally, 3-h forecasts are launched every hour during that period. The performance of this method is examined for two selected high-impact severe thunderstorm events: namely, the 24 May 2011 Oklahoma and 16 May 2017 Texas and Oklahoma tornado outbreaks. Relative to a control simulation that only assimilated radar data, the analyses and forecasts of these supercells (reflectivity patterns, tracks, and updraft helicity tracks) are qualitatively and quantitatively improved in both cases when the water vapor information is added into the analysis. [ABSTRACT FROM AUTHOR]

This study investigated the characteristics of extreme precipitation events associated with mesoscale convective systems (MCSs) in East China (the area east of 96°E) during 2016–17. Over the entire region, 204 events were first identified and classified into synoptic, tropical, MCS, small-scale-storm (SSS), and unclassified types. For 73 MCS-type events, further division and analysis were conducted according to the organizational modes. Results show that MCS-related events occurred most frequently near southern Fujian Province and from April to October with a peak in July. The area of occurrence shifted from the south in spring to the north in summer before going back to the south in autumn. The events occurred most commonly from afternoon to early evening, matured around late afternoon, and ended before dark. Among MCS subcategories, the longest average duration was seen in the multiple-MCS cases. Of the 15 selected multiple-MCS events, 11 were defined as early-maturing type with peak rainfall occurrence before the midpoint of duration while the others were late maturing. Although multiple-MCS events were accompanied by a southwest low-level jet, strong warm-air advection, and convective instability, early-maturing cases had stronger synoptic-scale ascent, moister environments, and smaller surface-based convective available potential energy (SBCAPE) and convection inhibition (SBCIN) at the most extreme rainfall-occurrence point. Compared to the MCS type within all extreme precipitation events over the United States, the percentage was lower in China. However, the events in China exhibit more pronounced seasonal cycle. [ABSTRACT FROM AUTHOR]

Aerosol optical depth (AOD) is the most basic parameter that describes the optical properties of atmospheric aerosols, and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the Fengyun-3A (FY-3A) and MODIS meteorological satellite using the Gridpoint Statistical Interpolation three-dimensional variational data assimilation system. Experiments were conducted for a dust storm over East Asia in April 2011. Each 0600 UTC analysis initialized a 24-hWeather Research and Forecasting with Chemistry model forecast. The results generally showed that the assimilation of satellite AOD observational data can significantly improve model aerosol mass prediction skills. The AOD distribution of the analysis field was closer to the observations of the satellite after assimilation of satellite AOD data. In addition, the analysis resulting from the experiment assimilating both FY-3A/MERSI (Medium-resolution Spectral Imager) AOD data and MODIS AOD data had closer agreement with the ground-based values than the individual assimilation of the two datasets for the dust storm over East Asia. These results suggest that the Chinese FY-3A satellite aerosol products can be effectively applied to numerical models and dust weather analysis. [ABSTRACT FROM AUTHOR]

The impact of stochastically perturbed parameterizations on short-term tornadic supercell ensemble forecasts (EFs) was evaluated using two tornado cases that occurred in eastern China. The initial condition (IC) perturbations of EFs were generated by a three-dimensional variational data assimilation system with perturbed radar data. The parameterization perturbations of EFs were produced by a stochastic procedure that was applied to diffusion and microphysics parameterizations. This procedure perturbed tendencies from both parameterizations and intercept parameters (INTCPs) of the microphysics parameterizations. In addition to individually perturbing these quantities, a combination of perturbations of diffusion and INTCPs was also examined. A resampling method was proposed to handle perturbations that vary substantially, and a vertical localization was applied to the microphysics tendency perturbations. The results indicated that combining perturbations of diffusion and INTCPs produced the intensity and path forecasts of the low-level vortex (LLV) that better match observations for a weak tornado case; this combination also had a positive impact on the LLV intensity forecast for a much stronger tornado case. This combination outperformed the stochastic procedures that perturbed only diffusion or INTCPs, which indicated that it is better to use both error representations. The vertical localization prevented the temperature tendency perturbations of microphysics from always suppressing storms in negative perturbation (<0.0) areas. The negative INTCP and diffusion perturbations benefited the strong LLV, which is consistent with that of the idealized case. The current stochastic procedure could not address the LLV displacement error that is caused by the IC error. [ABSTRACT FROM AUTHOR]

This study examines the impacts of assimilating radar radial velocity (Vr) data for the simulation of hurricane Ike (2008) with two different ensemble generation techniques in the framework of the hybrid ensemble-variational (EnVar) data assimilation system of Weather Research and Forecasting model. For the generation of ensemble perturbations we apply two techniques, the ensemble transform Kalman filter (ETKF) and the ensemble of data assimilation (EDA). For the ETKF-EnVar, the forecast ensemble perturbations are updated by the ETKF, while for the EDA-EnVar, the hybrid is employed to update each ensemble member with perturbed observations. The ensemble mean is analyzed by the hybrid method with flow-dependent ensemble covariance for both EnVar. The sensitivity of analyses and forecasts to the two applied ensemble generation techniques is investigated in our current study. It is found that the EnVar system is rather stable with different ensemble update techniques in terms of its skill on improving the analyses and forecasts. The EDA-EnVar-based ensemble perturbations are likely to include slightly less organized spatial structures than those in ETKF-EnVar, and the perturbations of the latter are constructed more dynamically. Detailed diagnostics reveal that both of the EnVar schemes not only produce positive temperature increments around the hurricane center but also systematically adjust the hurricane location with the hurricane-specific error covariance. On average, the analysis and forecast from the ETKF-EnVar have slightly smaller errors than that from the EDA-EnVar in terms of track, intensity, and precipitation forecast. Moreover, ETKF-EnVar yields better forecasts when verified against conventional observations. [ABSTRACT FROM AUTHOR]

Using radar observations, the performances of the ensemble square root filter (EnSRF) and an indirect three-dimensional variational (3DVar) data assimilation method were compared for a mesoscale convective system (MCS) that occurred in the Front Range of the Rocky Mountains, Colorado (USA). The results showed that the root mean square innovations (RMSIs) of EnSRF were lower than 3DVar for radar reflectivity and radial velocity and that the spread of EnSRF was generally consistent with its RMSIs. EnSRF substantially improved the analysis of the MCS compared with an experiment without radar data assimilation, and it produced a slight but noticeable improvement over 3DVar in terms of both coverage and intensity. Forecast results initiated from the final analysis revealed that EnSRF generally produced the best prediction of the MCS, with improved quantitative reflectivity and precipitation forecast skills. EnSRF also demonstrated better performance than 3DVar in the prediction of neighborhood probability for reflectivity at thresholds of 20 and 35 dBZ, which better matched the observed radar reflectivity in terms of both shape and extension. Additionally, the humidity, temperature, and wind fields were also improved by EnSRF; the largest error reduction was found in the water vapor field near the surface and at upper levels. [ABSTRACT FROM AUTHOR]

Abstract: Himawari‐8 is the first launched and operational new‐generation geostationary meteorological satellite. The Advanced Himawari Imager (AHI) on board Himawari‐8 provides continuous high‐resolution observations of severe weather phenomena in space and time. In this study, the capability to assimilate AHI radiances has been developed within the Weather Research and Forecasting (WRF) model's data assimilation system. As the first attempt to assimilate AHI using WRF data assimilation at convective scales, the added value of hourly AHI clear‐sky radiances from three water vapor channels on convection‐permitting (3 km) analyses and forecasts of the “7.19” severe rainstorm that occurred over north China during 18–21 July 2016 was investigated. Analyses were produced hourly, and 24 h forecasts were produced every 6 h. The results showed that improved wind and humidity fields were obtained in analyses and forecasts verified against conventional observations after assimilating AHI water vapor radiances when compared to the control experiment which assimilated only conventional observations. It was also found that the assimilation of AHI water vapor radiances had a clearly positive impact on the rainfall forecast for the first 6 h lead time, especially for heavy rainfall exceeding 100 mm when verified against the observed rainfall. Furthermore, the horizontal and vertical distribution of features in the moisture fields were improved after assimilating AHI water vapor radiances, eventually contributing to a better forecast of the severe rainstorm. [ABSTRACT FROM AUTHOR]

Radar reflectivity observations contain valuable information on precipitation and have been assimilated into numerical weather prediction models for improved microphysics initialization. However, low-reflectivity (or so-called no rain) echoes have often been ignored or not effectively used in radar data assimilation schemes. In this paper, a scheme to assimilate no-rain radar observations is described within the framework of the Weather Research and Forecasting Model's three-dimensional variational data assimilation (3DVar) system, and its impact on precipitation forecasts is demonstrated. The key feature of the scheme is a neighborhood-based approach to adjusting water vapor when a grid point is deemed as no rain. The performance of the scheme is first examined using a severe convective case in the Front Range of the Colorado Rocky Mountains and then verified by running the 3DVar system in the same region, with and without the norain assimilation scheme for 68 days and 3-hourly rapid update cycles. It is shown that the no-rain data assimilation method reduces the bias and false alarm ratio of precipitation over its counterpart without that assimilation. The no-rain assimilation also improved humidity, temperature, and wind fields, with the largest error reduction in the water vapor field, both near the surface and at upper levels. It is also shown that the advantage of the scheme is in its ability to conserve total water content in cycled radar data assimilation, which cannot be achieved by assimilating only precipitation echoes. [ABSTRACT FROM AUTHOR]

A mesoscale convective system (MCS) over East China on June 5, 2009 was thoroughly analyzed using an Advanced Regional Prediction System (ARPS) ensemble square root filter (EnSRF) system. The analyzed reflectivity structure, location and intensity compared well with observation, and were substantially better than an experiment without radar data assimilation. The cold pool and wind speed in the convective regions were strengthened. With improved initial conditions, the impact of single‐moment (SM), double‐moment (DM) and triple‐moment (TM) microphysics parameterization (MP) schemes on ensemble forecasts of MCSs was evaluated. The use of multi‐moment (MM) MP schemes showed some improvements in neighborhood ensemble probability for reflectivity and precipitation. Quantitative reflectivity and precipitation forecast skills were also improved in MM forecasts, with those of the TM forecast the best. [ABSTRACT FROM AUTHOR]

The concept of stochastic parameterization provides an opportunity to represent spatiotemporal errors caused by microphysics schemes that play important roles in supercell simulations. In this study, two stochastic methods, the stochastically perturbed temperature tendency from microphysics (SPTTM) method and the stochastically perturbed intercept parameters of microphysics (SPIPM) method, are implemented within the Lin scheme, which is based on the Advanced Regional Prediction System (ARPS) model, and are tested using an idealized supercell case. The SPTTM and SPIPM methods perturb the temperature tendency and the intercept parameters (IPs), respectively. Both methods use recursive filters to generate horizontally smooth perturbations and adopt the barotropic structure for the perturbation r, which is multiplied by tendencies or parameters from this parameterization. A double-moment microphysics scheme is used for the truth run. Compared to the multiparameter method, which uses randomly perturbed prescribed parameters, stochastic methods often produce larger ensemble spreads and better forecast the intensity of updraft helicity (UH). The SPTTM method better predicts the intensity by intensifying the midlevel heating with its positive perturbation r, whereas it performs worse in the presence of negative perturbation. In contrast, the SPIPM method can increase the intensity of UH by either positive or negative perturbation, which increases the likelihood for members to predict strong UH. [ABSTRACT FROM AUTHOR]

ABSTRACT The ensemble transform Kalman filter based ensemble 3D variational ( ETKF-En3DVAR) data assimilation ( DA) system is employed to evaluate the potential value of assimilating radar radial wind ( Vr) data for the analysis and forecasting of Typhoon Saomai (2006). The DA system conducted cycling assimilation every 30 min when Saomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF-En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow-dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of Vr data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR. The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow-dependent background error covariance. [ABSTRACT FROM AUTHOR]

The impacts of different microphysics and boundary schemes and terrain settings on the heavy rainfall over western Myanmar associated with the tropical cyclone (TC) ROANU (2016) are investigated using the Weather Research and Forecasting (WRF) model. The results show that the microphysics scheme of Purdue Lin (LIN) scheme produces the strongest cyclone. Six experiments with various combinations of microphysics and boundary schemes indicated that a combination of WRF Single-Moment 6-class (WSM6) scheme and Mellor-Yamada-Janjic (MYJ) best fits to the Joint Typhoon Warning Center (JTWC) data. WSM6-MYJ also performs the best for the track and intensity of rainfall and obtains the best statistics skill scores in the range of maximum rainfall intensity for 48-h. Sensitivity experiments on different terrain settings with Normal Rakhine Mountain (NRM), with Half of Rakhine Mountain (HRM), and Without Rakhine Mountain (WoRM) are designed with the use of WSM6-MYJ scheme. The track of TC ROANU moved northwestward in WoRM and HRM. Due to the presence of Rakhine Mountain, TC track moved into Myanmar and the peak rainfall occurred on the leeward side of the Mountain. In the absence of Rakhine Mountain, a shift in peak rainfall was observed in north side of the Mountain. [ABSTRACT FROM AUTHOR]

Diffusion plays an important role in supercell simulations. A stochastically perturbed parameterization tendency scheme for diffusion (SPPTD) is developed to incorporate diffusive uncertainties in ensemble forecasts. This scheme follows the same procedure as the previously published stochastically perturbed parameterization tendencies (SPPT) scheme but uses a recursive filter to generate smooth perturbations. It also employs horizontal and vertical localization to retain the impact of perturbation in areas with strong shear. Three additional restrictions are added for the sake of integration stability; these restrictions determine the area and amplitude of the perturbation and the situation to suspend SPPTD. The performance of this scheme is examined by using an idealized supercell storm. The model errors are simulated using different resolutions in the truth run (1 km) and ensemble forecasts (2 km). The results indicate that the ensemble forecasts using SPPTD encompass the intensity and displacement of maximum updraft helicity in the truth run. This scheme yields better results than can be obtained using initial perturbations or larger computational mixing coefficients. The sensitivity of SPPTD to each of its parameters is also examined. The results indicate that the optimal horizontal and temporal scales for SPPTD are 40 km and 30 min, respectively. Moderately adjusting the spatiotemporal scale by 10 km or 10 min does not significantly change the SPPTD performance. In this case study, an ensemble size of 20 is sufficient. Perturbing the diffusion terms of all variables using the same approach does not provide additional benefits other than that of selected variables and thus requires further study. [ABSTRACT FROM AUTHOR]

Hydrometeor variables (cloud water and cloud ice mixing ratios) are added into the WRF three-dimensional variational assimilation system as additional control variables to directly analyze hydrometeors by assimilating cloud observations. In addition, the background error covariance matrix of hydrometeors is modeled through a control variable transform, and its characteristics discussed in detail. A suite of experiments using four microphysics schemes (LIN, SBU-YLIN, WDM6 and WSM6) are performed with and without assimilating satellite cloud liquid/ice water path. We find analysis of hydrometeors with cloud assimilation to be significantly improved, and the increment and distribution of hydrometeors are consistent with the characteristics of background error covariance. Diagnostic results suggest that the forecast with cloud assimilation represents a significant improvement, especially the ability to forecast precipitation in the first seven hours. It is also found that the largest improvement occurs in the experiment using the WDM6 scheme, since the assimilated cloud information can sustain for longer in this scheme. The least improvement, meanwhile, appears in the experiment using the SBU-YLIN scheme. [ABSTRACT FROM AUTHOR]

Atmospheric moisture content or humidity is an important analysis variable of any meteorological data assimilation system. The humidity analysis can be univariate, using humidity background (normally short-range numerical forecasts) and humidity observations. However, more and more data assimilation systems are multivariate, analyzing humidity together with wind, temperature and pressure. Background error covariances, with unbalanced velocity potential and humidity in the multivariate formulation, are generated from weather research and forecasting model forecasts, collected over a summer rainy season and a winter dry season. The unbalanced velocity potential and humidity related correlations are shown to be significantly larger, indicating more important roles unbalanced velocity potential and humidity play, in the rainy season than that in the dry season. Three cycling data assimilation experiments of two rainfall events in the middle and lower reaches of the Yangtze River are carried out. The experiments differ in the formulation of the background error covariances. Results indicate that only including unbalanced velocity potential in the multivariate background error covariance improves wind analyses, but has little impact on temperature and humidity analyses. In contrast, further including humidity in the multivariate background error covariance although has a slight negative effect on wind analyses and a neutral effect on temperature analyses, but significantly improves humidity analyses, leading to precipitation forecasts more consistent with China Hourly Merged Precipitation Analysis. [ABSTRACT FROM AUTHOR]

In this study, a 600-yr integration performed with the Bergen Climate Model (BCM), version 2.0, was used to investigate the impact of strong tropical volcanic eruptions (SVEs) on the East Asian winter monsoon (EAWM). It is found that SVEs have an important influence on the East Asian winter climate. The volcanic forcing can cause changes in surface heat fluxes and tropospheric circulation, particularly over the tropics and high-latitude regions. As a result, Arctic Oscillation enters into its positive phase in the first two winters after SVEs. The associated circulation weakens the Siberian high and reduces the cold air transport to East Asia, which is not conducive to the enhancement of the EAWM during this period. At the same time, the North Pacific Ocean gradually cools and shapes La Niña-like sea surface temperature (SST) anomalies in the third winter after SVEs. The Walker circulation is strengthened over the Pacific, and two anomalous lower-tropospheric cyclones are located over the South China Sea and southeast of Japan, respectively. Therefore, related northeasterly wind anomalies appear along the East Asian coast, indicating a strengthened EAWM during this period. Meanwhile, the enhanced Siberian high and East Asian trough further contribute to the enhancement of EAWM in the third winter. It is therefore concluded that the SVEs-induced climate changes over the tropical Pacific and north polar regions play an important role in regulating the EAWM in the posteruption winters. [ABSTRACT FROM AUTHOR]

Chan-hom and Linfa were binary typhoons that occurred in the western North Pacific in 2015. In this study, the impacts of FY-3B satellite Microwave Humidity Sounder (MWHS) radiance observations on the analyses and forecasts of Linfa and Chan-hom are assessed. The regional Weather Research and Forecasting model and its data assimilation (DA) systems, using three-dimensional variational (3DVAR) and Hybrid (ensemble/3DVAR) methods are used. Assimilation of the FY-3B MWHS data using the 3DVAR method slightly improves the descriptive wind and temperature fields. Positive impacts on the specific humidity forecasts, for levels higher than 850 hPa, are also obvious. 3DVAR adjusts the typhoons' initial positions and their dynamic structures favorably, yielding better tracks, intensities, and precipitation forecasts, compared to the experiment run without MWHS data (control). With the Hybrid method, the water vapor information from the MWHS data better improve the analyses through multivariable correlations with the flow-dependent background error. The Hybrid method further improves the track, intensities, and precipitation forecasts. For Typhoon Linfa, with the coexistence of Typhoon Chan-hom, the Hybrid method provides a more descriptive background error covariance matrix, than using 3DVAR. Experiments on multiple binary typhoon cases are also provided to further validate the robustness of the results on the FY-3B satellite MWHS radiance data assimilation. [ABSTRACT FROM AUTHOR]

ABSTRACT On the basis of mean temperature, maximum temperature and minimum temperature from the updated China Homogenized Historical Temperature Data Sets, the recent warming in the Tibetan Plateau ( TP) during 1961-2005 and global warming hiatus period are examined. During 1961-2005, the mean temperature, maximum temperature and minimum temperature in the whole TP show a statistically increasing trend especially after the 1980s, with the annual rates of 0.27, 0.19 and 0.36 °C decade−1, respectively. The performance of 26 general circulation models ( GCMs) available in the fifth phase of the Coupled Model Intercomparison Project ( CMIP5) is evaluated in the TP by comparison with the observations during 1961-2005. Most CMIP5 GCMs can capture the decadal variations of the observed mean temperature, maximum temperature and minimum temperature, and have significant positive correlations with observations ( R > 0.5), with root mean squared error <1 °C. This suggests that CMIP5 GCMs can reproduce the recent temperature evolution in the TP, but with cold biases. However, most CMIP5 GCMs underestimate the observed warming rates, especially the CNRM-CM5, GISS-E2-H and MRI-CGCM3 models. There are significant positive correlations between the trend magnitudes and the anomaly of the mean temperature, maximum temperature and minimum temperature, with correlations of 0.85, 0.86 and 0.87, respectively. The warming from the observations and CMIP5 mean in the TP is significant during the global hiatus period, consistent with decreasing snow cover and albedo in the region. This study suggests that positive snow/ice-albedo feedback processes may account for ongoing surface warming in the TP despite the pause in global mean surface warming. [ABSTRACT FROM AUTHOR]

ABSTRACT Spatial and temporal variabilities of long-term (1961-2013) diurnal temperature range ( DTR) are examined in the Tibetan Plateau ( TP) based on the 71 observational stations. The relative regional contributions to DTR in the TP are studied among maximum temperature, minimum temperature, total cloud cover ( TCC), and atmospheric teleconnections. The regional annual mean DTR (average of the 71 stations) is 14.17 °C, with a clear maximum in winter (16.35 °C) and minimum in summer (12.62 °C). During 1961-2013, the DTR in the TP declines before the 1980s and shows mute change afterwards, with an annual rate of −0.20 °C decade−1 calculated by the Mann-Kendall method. The trend in DTR is primarily a consequence of greater warming in minimum temperature than maximum temperature. In summer, there are significant negative correlations between the TCC and DTR in the TP, suggesting that the decreases in the DTR are associated with variations of TCC in the region. The atmospheric circulation composite analysis between strongly positive and negative DTR in summer in the TP reveals that during the low DTR period the TP has more water vapour flux, stronger temperature advection, and strengthened southerly wind. This suggests that the atmospheric circulations have contributed to the trends in the DTR, but it is difficult to account for the specific contributions. Further investigations of the impact of global warming on the DTR in the TP are still required. [ABSTRACT FROM AUTHOR]

A method to assimilate all-sky radiances from the Advanced Microwave Scanning Radiometer 2 (AMSR2) was developed within the Weather Research and Forecasting (WRF) model's data assimilation (WRFDA) system. The four essential elements are: (1) extending the community radiative transform model's (CRTM) interface to include hydrometeor profiles; (2) using total water Qt as the moisture control variable; (3) using a warm-rain physics scheme for partitioning the Qt increment into individual increments of water vapour, cloud liquid water and rain; and (4) adopting a symmetric observation error model for all-sky radiance assimilation. Compared to a benchmark experiment with no AMSR2 data, the impact of assimilating clear-sky or allsky AMSR2 radiances on the analysis and forecast of Hurricane Sandy (2012) was assessed through analysis/ forecast cycling experiments using WRF and WRFDA's three-dimensional variational (3DVAR) data assimilation scheme. With more cloud/precipitation-affected data being assimilated around tropical cyclone (TC) core areas in the all-sky AMSR2 assimilation experiment, better analyses were obtained in terms of the TC's central sea level pressure (CSLP), warm-core structure and cloud distribution. Substantial (~20 %) error reduction in track and CSLP forecasts was achieved from both clear-sky and all-sky AMSR2 assimilation experiments, and this improvement was consistent from the analysis time to 72-h forecasts. Moreover, the allsky assimilation experiment consistently yielded better track and CSLP forecasts than the clear-sky did for all forecast lead times, due to a better analysis in the TC core areas. Positive forecast impact from assimilating AMSR2 radiances is also seen when verified against the European Center for Medium-Range Weather Forecasts (ECMWF) analysis and the Stage IV precipitation analysis, with an overall larger positive impact from the all-sky assimilation experiment. [ABSTRACT FROM AUTHOR]