Rainwater Mixing Ratio Research Articles

The Improvement of Short-Term Quantitative Precipitation Forecast in Mountainous Areas by the Assimilation of Meteorological State Variables Retrieved by Multiple Doppler Radar Data

Abstract This study presents a sequential procedure formulated by combining a multiple-Doppler radar wind synthesis technique with a thermodynamic retrieval method, which can be applied to retrieve the three-dimensional wind, pressure, temperature, rainwater mixing ratio, and moisture over complex terrain. The retrieved meteorological state variables are utilized to reinitialize a high-resolution numerical model, which then carries out time integration using four different microphysical (MP) schemes, including the Goddard Cumulus Ensemble (GCE), Morrison (MOR), WRF single-moment 6-class (WSM6), and WRF double-moment 6-class (WDM6) schemes. It is found that through this procedure, the short-term quantitative precipitation forecast (QPF) skill of a numerical model over mountainous areas can be significantly improved up to 6 h. The moisture field plays a crucial role in producing the correct rainfall forecast. Since no specific microphysical scheme outperforms the others, a combination of various rainfall scenarios forecasted by different MP schemes is suggested in order to provide a stable and reliable rainfall forecast. This work also demonstrates that, with the proposed approach, radar data from only two volume scans are sufficient to improve the rainfall forecasts. This is because the unobserved meteorological state variables are instantaneously retrieved and directly used to reinitialize the model, thereby the model spinup time can be effectively shortened.

Improving Simulations of Warm Rain in a Bulk Microphysics Scheme

Abstract Current bulk microphysical parameterization schemes underpredict precipitation intensities and drop size distributions (DSDs) during warm rain periods, particularly upwind of coastal terrain. To help address this deficiency, this study introduces a set of modifications, called RCON, to the liquid-phase (warm rain) parameterization currently used in the Thompson–Eidhammer microphysical parameterization scheme. RCON introduces several model modifications, motivated by evaluating simulations from a bin scheme, which together result in more accurate precipitation simulations during periods of warm rain. Among the most significant changes are 1) the use of a wider cloud water DSD of lognormal shape instead of the gamma DSD used by the Thompson–Eidhammer parameterization and 2) enhancement of the cloud-to-rain autoconversion parameterization. Evaluation of RCON is performed for two warm rain events and an extended period during the Olympic Mountains Experiment (OLYMPEX) field campaign of winter 2015/16. We show that RCON modifications produce more realistic precipitation distributions and rain DSDs than the default Thompson–Eidhammer configuration. For the multimonth OLYMPEX period, we show that rain rates, rainwater mixing ratios, and raindrop number concentrations were increased relative to the Thompson–Eidhammer microphysical parameterization, while concurrently decreasing raindrop diameters in liquid-phase clouds. These changes are consistent with an increase in simulated warm rain. Finally, real-time evaluation of the scheme from August 2021 to August 2022 demonstrated improved precipitation prediction over coastal areas of the Pacific Northwest. Significance Statement Although the accurate simulation of warm rain is critical to forecasting the hydrology of coastal areas and windward slopes, many warm rain parameterizations underpredict precipitation in these locations. This study introduces and evaluates modifications to the Thompson–Eidhammer microphysics parameterization scheme that significantly improve the accuracy of rainfall prediction in those regions.

Long-term characteristics of hydrometeors in stratiform and convective precipitation over central eastern China and adjacent ocean

Based on the fifth generation atmospheric reanalysis (ERA5) hourly data from May to August during 1979–2020, the long-term features of four hydrometeors (cloud water, cloud ice, rain, and snow) in stratiform and convective precipitation over the central eastern China and its adjacent northwest Pacific Ocean, as well as their relationship with precipitation intensity were first investigated. Results show that the ERA5 hydrometeor data is generally reasonable over the study regions by comparing with 7-yr GPM retrievals. In stratiform precipitation, the cloud water profile presents a double-peak structure over land and ocean. The distribution of precipitation intensity is in agreement with that of cloud ice path, indicating that stratiform precipitation is more dominated by ice processes, and the deposition latent heating is the main heat source. In convective precipitation, the rain water mixing ratio is distinctly greater over ocean than that over land due to the higher conversion rate from cloud droplets to raindrops, along with abundant snow water there. The pattern of convective precipitation intensity is more consistent with that of rain water path, and the ice-phase hydrometeor path is 1–2 times that of liquid-phase hydrometeor, suggesting a comparable contribution of water and ice processes. The condensation latent heating with its upward transport is the main heat source in convection. It's found that the conversion rate from rain water to surface rainfall is higher in convective than in stratiform regimes, and is faster over land than over ocean, which should be attributed to the larger-size raindrops in the former. In addition, the continental precipitation diurnal circle exhibits a bimodal structure, while the oceanic precipitation has only a single peak. The peaks of ice-phase hydrometeors lag behind the peak of precipitation in the afternoon owing to the different moisture dynamic structures.

Diagnostic Relations for the Intercept Parameter of Exponential Raindrop Size Distribution According to Rain Types Derived from Disdrometer Data and Their Impacts on Precipitation Prediction

The raindrop size distribution observed from ground-based or airborne disdrometers has been widely used to understand the characteristics of clouds and precipitation. However, its variability needs to be studied further and properly considered for improving precipitation prediction. In this study, using disdrometer data, the diagnostic relations for the intercept parameter of the exponential raindrop size distribution N0 are derived for different rain types and the impacts of the diagnostic relations on precipitation prediction are examined. The disdrometer data observed at four sites in South Korea show spatiotemporal variations of N0. Three different derivation methods proposed by previous studies are used to derive the diagnostic relations, and the diagnostic relation that best reproduces the observed N0 is selected. The diagnostic relation is implemented into the WRF single-moment 6-class microphysics (WSM6) scheme, and its impacts are investigated through the simulations of summertime precipitation events in South Korea. Compared to the simulation using the original WSM6 scheme (WSM6-O) where a constant N0 is used, the simulation where N0 is diagnosed by the diagnostic relation using the rainwater content at the lowest level (WSM6-L) yields better precipitation prediction. The WSM6-L simulation represents the variability of N0. Also, the WSM6-L simulation predicts N0 that is on average smaller than the prescribed value in the WSM6-O simulation, agreeing with the observation to some extent. The smaller N0 in the WSM6-L simulation decreases the rainwater production by the accretion of cloud water and the melting of ice hydrometeors, decreasing the rainwater mixing ratio.

Impact of Different Double-Moment Microphysical Schemes on Simulations of a Bow-Shaped Squall Line in East China

Four typical double-moment cloud microphysical schemes (Milbrandt, Morrison, NSSL and WDM6) in WRF (Weather Research and Forecasting) are used to investigate the impact of the different schemes on the simulation of a straight squall line evolving into a bow-shaped one in East China on 15 April 2016. Although simulations with Milbrandt, Morrison and WDM6 schemes can produce bow-shaped squall lines, only the WDM6 scheme can simulate the evolution process of the straight squall line to a bow-shaped one well. The simulation results with the NSSL scheme produce a broken straight squall line. The possible reason is that the range and intensity of cold pools and the rear inflows are different among the four schemes when the observed squall line evolves into a standard bow-shaped squall line. The mixing ratio and number concentration of rain water with the WDM6 scheme is largest in strong convective areas, and the cold pool and rear inflow are also strongest among the four schemes. Compared with the WDM6 scheme, the mixing ratio and number concentration of rain water are less with the Milbrandt and Morrison schemes. Also, the cold pool and rear inflow with the Milbrandt scheme are weaker than for the WDM6 scheme but stronger than those with the Morrison scheme. The rainwater mixing ratio is very low and no obvious cold pool and rear inflow exist with the NSSL scheme.

Limitations of Bin and Bulk Microphysics in Reproducing the Observed Spatial Structure of Light Precipitation

Abstract Coarse-gridded atmospheric models often account for subgrid-scale variability by specifying probability distribution functions (PDFs) of process rate inputs such as cloud and rainwater mixing ratios (qc and qr, respectively). PDF parameters can be obtained from numerous sources: in situ observations, ground- or space-based remote sensing, or fine-scale modeling such as large-eddy simulation (LES). LES is appealing to constrain PDFs because it generates large sample sizes, can simulate a variety of cloud regimes/case studies, and is not subject to the ambiguities of observations. However, despite the appeal of using model output for parameterization development, it has not been demonstrated that LES satisfactorily reproduces the observed spatial structure of microphysical fields. In this study, the structure of observed and modeled microphysical fields are compared by applying bifractal analysis, an approach that quantifies variability across spatial scales, to simulations of a drizzling stratocumulus field that span a range of domain sizes, drop concentrations (a proxy for mesoscale organization), and microphysics schemes (bulk and bin). Simulated qc closely matches observed estimates of bifractal parameters that measure smoothness and intermittency. There are major discrepancies between observed and simulated qr properties, though, with bulk simulated qr consistently displaying the bifractal properties of observed clouds (smooth, minimally intermittent) rather than rain while bin simulations produce qr that is appropriately intermittent but too smooth. These results suggest fundamental limitations of bulk and bin schemes to realistically represent higher-order statistics of the observed rain structure.

Combination of wind gust models in convective events

Gusts are particularly relevant to wind engineering and it is of interest to develop a forecasting tool for wind energy management for systems such as Uruguay’s, which has a wind power participation of 38% (UTE, 2019). In the present work, we assess the performance of two gust parameterizations (Gutiérrez–Fovell and Nakamura et al.) utilizing Weather Research and Forecasting (WRF) model simulations for predicting gusts at 100 ​m above ground level in the presence of convection. Convective activity is predicted when the vertically accumulated rain water mixing ratio computed by the model exceeds a selected value and is verified in part via satellite imagery (GOES-13). Gust forecast skill is evaluated with wind tower observations. We find a combination of the two parameterizations yields the highest forecast skill.

Application of a Physical Ensemble Method in the POD-4DEnVar

Abstract The sensitivity of the proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation (4DVar) method (referred to as POD-4DEnVar) to cumulus and microphysics schemes was investigated using the Weather Research and Forecasting (WRF) Model for heavy rainfall in South China. Results show that the choice of the cumulus and microphysics schemes for ensemble samples significantly impacts precipitation prediction and that Doppler radar data assimilation using POD-4DEnVar is sensitive to the parameterization schemes used for the ensemble samples. The cumulus and microphysics schemes primarily affect the vertical velocity and rainwater mixing ratio of the ensemble forecasts. Variations in the ensemble samples caused by different parameterization schemes are introduced into the four-dimensional ensemble variational assimilation by the radar data observation operator. These variations affect the analysis fields and result in variations in precipitation prediction. To obtain the optimal result (smallest forecast error), three methods are designed based on the physical ensemble technique, which can filter out the effects of different parameterization schemes for the ensemble samples through averaging. The results show that the precipitation forecasts from the three assimilation experiments are improved compared with a control experiment, but each physical ensemble method leads to a unique precipitation forecast.

Parameterization of the Spatial Variability of Rain for Large-Scale Models and Remote Sensing

AbstractThe spatial variability of rain rate R is evaluated by using both radar observations and cloud-resolving model output, focusing on the Tropical Warm Pool–International Cloud Experiment (TWP-ICE) period. In general, the model-predicted rain-rate probability distributions agree well with those estimated from the radar data across a wide range of spatial scales. The spatial variability in R, which is defined according to the standard deviation of R (for R greater than a predefined threshold Rmin) σ(R), is found to vary according to both the average of R over a given footprint μ(R) and the footprint size or averaging scale Δ. There is good agreement between area-averaged model output and radar data at a height of 2.5 km. The model output at the surface is used to construct a scale-dependent parameterization of σ(R) as a function of μ(R) and Δ that can be readily implemented into large-scale numerical models. The variability in both the rainwater mixing ratio qr and R as a function of height is also explored. From the statistical analysis, a scale- and height-dependent formulation for the spatial variability of both qr and R is provided for the analyzed tropical scenario. Last, it is shown how this parameterization can be used to assist in constraining parameters that are often used to describe the surface rain-rate distribution.

Indian summer monsoon rainfall: Dancing with the tunes of the sun

There is strong statistical evidence that solar activity influences the Indian summer monsoon rainfall. To search for a physical link between the two, we consider the coupled cloud hydrodynamic equations, and derive an equation for the rate of precipitation that is similar to the equation of a forced harmonic oscillator, with cloud and rain water mixing ratios as forcing variables. Those internal forcing variables are parameterized in terms of the combined effect of external forcing as measured by sunspot and coronal hole activities with several well known solar periods (9, 13 and 27days; 1.3, 5, 11 and 22years). The equation is then numerically solved and the results show that the variability of the simulated rate of precipitation captures very well the actual variability of the Indian monsoon rainfall, yielding vital clues for a physical understanding that has so far eluded analyses based on statistical correlations alone. We also solved the precipitation equation by allowing for the effects of long-term variation of aerosols. We tentatively conclude that the net effects of aerosols variation are small, when compared to the solar factors, in terms of explaining the observed rainfall variability covering the full Indian monsoonal geographical domains.

Impact of Doppler weather radar data on thunderstorm simulation during STORM pilot phase—2009

This study assesses the impact of Doppler weather radar (DWR) data (reflectivity and radial wind) assimilation on the simulation of severe thunderstorms (STS) events over the Indian monsoon region. Two different events that occurred during the Severe Thunderstorms Observations and Regional Modeling (STORM) pilot phase in 2009 were simulated. Numerical experiments—3DV (assimilation of DWR observations) and CNTL (without data assimilation)—were conducted using the three-dimensional variational data assimilation technique with the Advanced Research Weather Research and Forecasting model (WRF-ARW). The results show that consistent with prior studies the 3DV experiment, initialized by assimilation of DWR observations, performed better than the CNTL experiment over the Indian region. The enhanced performance was a result of improved representation and simulation of wind and moisture fields in the boundary layer at the initial time in the model. Assimilating DWR data caused higher moisture incursion and increased instability, which led to stronger convective activity in the simulations. Overall, the dynamic and thermodynamic features of the two thunderstorms were consistently better simulated after ingesting DWR data, as compared to the CNTL simulation. In the 3DV experiment, higher instability was observed in the analyses of thermodynamic indices and equivalent potential temperature (θ e) fields. Maximum convergence during the mature stage was also noted, consistent with maximum vertical velocities in the assimilation experiment (3DV). In addition, simulated hydrometeor (water vapor mixing ratio, cloud water mixing ratio, and rain water mixing ratio) structures improved with the 3DV experiment, compared to that of CNTL. From the higher equitable threat scores, it is evident that the assimilation of DWR data enhanced the skill in rainfall prediction associated with the STS over the Indian monsoon region. These results add to the body of evidence now which provide consistent and notable improvements in the mesoscale model results over the Indian monsoon region after assimilating DWR fields.

Impact of cloud microphysics and cumulus parameterization on simulation of heavy rainfall event during 7–9 October 2007 over Bangladesh

In the present study, the Advanced Research WRF (ARW) version 3.2.1 has been used to simulate the heavy rainfall event that occurred between 7 and 9 October 2007 in the southern part of Bangladesh. Weather Research and Forecast (WRF–ARW version) modelling system with six different microphysics (MP) schemes and two different cumulus parameterization (CP) schemes in a nested configuration was chosen for simulating the event. The model domains consist of outer and inner domains having 9 and 3 km horizontal resolution, respectively with 28 vertical sigma levels. The impacts of cloud microphysical processes by means of precipitation, wind and reflectivity, kinematic and thermodynamic characteristics of the event have been studied. Sensitivity experiments have been conducted with the WRF model to test the impact of microphysical and cumulus parameterization schemes in capturing the extreme weather event. NCEP FNL data were used for the initial and boundary condition. The model ran for 72 h using initial data at 0000 UTC of 7 October 2007. The simulated rainfall shows that WSM6–KF combination gives better results for all combinations and after that Lin–KF combination. WSM3–KF has simulated, less area average rainfall out of all MP schemes that were coupled with KF scheme. The sharp peak of relative humidity up to 300 hPa has been simulated along the vertical line where maximum updraft has been found for all MPs coupled with KF and BMJ schemes. The simulated rain water and cloud water mixing ratio were maximum at the position where the vertical velocity and reflectivity has also been maximum. The production of rain water mixing ratio depends on MP schemes as well as CP schemes. Rainfall depends on rain water mixing ratio between 950 and 500 hPa. Rain water mixing ratio above 500 hPa level has no effect on surface rain.

A new ensemble-based data assimilation algorithm to improve track prediction of tropical cyclones

This paper proposes a new ensemble-based algorithm that assimilates the vertical rain structure retrieved from microwave radiometer and radar measurements in a regional weather forecast model, by employing a Bayesian framework. The goal of the study is to evaluate the capability of the proposed technique to improve track prediction of tropical cyclones that originate in the North Indian Ocean. For this purpose, the tropical cyclone Jal has been analyzed by the community mesoscale weather model, weather research and forecasting (WRF). The ensembles of prognostic variables such as perturbation potential temperature (θ, k), perturbation geopotential (ϕ, m2/s2), meridional (U) and zonal velocities (V) and water vapor mixing ratio (q v , kg/kg) are generated by the empirical orthogonal function technique. An over pass of the tropical rainfall-measuring mission (TRMM) satellite occurred on 06th NOV 0730 UTC over the system, and the observations from the radiometer and radar on board the satellite(1B11 data products) are inverted using a combined in-home radiometer-radar retrieval technique to estimate the vertical rain structure, namely the cloud liquid water, cloud ice, precipitation water and precipitation ice. Each ensemble is input as a possible set of initial conditions to the WRF model from 00 UTC which was marched in time till 06th NOV 0730 UTC. The above-mentioned hydrometeors from the cloud water and rain water mixing ratios are then estimated for all the ensembles. The Bayesian filter framework technique is then used to determine the conditional probabilities of all the candidates in the ensemble by comparing the retrieved hydrometeors through measured TRMM radiances with the model simulated hydrometeors. Based on the posterior probability density function, the initial conditions at 06 00 UTC are then corrected using a linear weighted average of initial ensembles for the all prognostic variables. With these weighted average initial conditions, the WRF model has been run up to 08th Nov 06 UTC and the predictions are then compared with observations and the control run. An ensemble independence study was conducted on the basis of which, an optimum of 25 ensembles is arrived at. With the optimum ensemble size, the sensitivity of prognostic variables was also analyzed. The model simulated track when compared with that obtained with the corrected set of initial conditions gives better results than the control run. The algorithm can improve track prediction up to 35 % for a 24 h forecast and up to 12 % for a 54 h forecast.

Impacts of Assimilating Measurements of Different State Variables with a Simulated Supercell Storm and Three-Dimensional Variational Method

Abstract This paper investigates the impacts of assimilating measurements of different state variables, which can be potentially available from various observational platforms, on the cycled analysis and short-range forecast of supercell thunderstorms by performing a set of observing system simulation experiments (OSSEs) using a storm-scale three-dimensional variational data assimilation (3DVAR) method. The control experiments assimilate measurements every 5 min for 90 min. It is found that the assimilation of horizontal wind can reconstruct the storm structure rather accurately. The assimilation of vertical velocity , potential temperature , or water vapor can partially rebuild the thermodynamic and precipitation fields but poorly retrieves the wind fields. The assimilation of rainwater mixing ratio can build up the precipitation fields together with a reasonable cold pool but is unable to properly recover the wind fields. Overall, data have the greatest impact, while have the second largest impact. The impact of is the smallest. The impact of assimilation frequency is examined by comparing results using 1-, 5-, or 10-min assimilation intervals. When is assimilated every 5 or 10 min, the analysis quality can be further improved by the incorporation of additional types of observations. When are assimilated every minute, the benefit from additional types of observations is negligible, except for . It is also found that for , , and measurements, more frequent assimilation leads to more accurate analyses. For and , a 1-min assimilation interval does not produce a better analysis than a 5-min interval.

Impacts of cloud flare-ups on hurricane intensity resulting from departures from balance laws

ABSTRACTCloud flare-ups along the inner eye wall of a hurricane lead to enhancement of cloud scale divergence, which in turn leads to a large local enhancement of the departure from balance laws and can lead to local supergradient winds. This scenario is tested using the results from a mesoscale microphysical model at horizontal resolution of 1.33 km for the simulation of hurricane Katrina. Rainwater mixing ratio tags growing cloud elements. The departure from balance laws includes terms such as the local, horizontal and vertical advections of divergence, divergence square and a term invoking the gradient of vertical velocity. It is noted that these terms collectively contribute to a substantial local enhancement of the departure from balance laws. Departures from balance laws are related to the radial gradient wind imbalances in a storm-centred coordinate. In this study, several examples, from the hurricane Katrina simulations, that display this scenario of rapid intensification are illustrated. Organisation of convection in the azimuthal direction seems important for the hurricane scale; cloud flare-ups away from such regions of azimuthal organisation fail to contribute to this scenario for the overall intensification of the hurricane.

Radar data assimilation of the GRAPES model and experimental results in a typhoon case

Constructing β-mesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction (NWP) model. Without vertical velocity matching the β-mesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrometeors. In this study, a method, basing on the three-dimensional variational (3DVAR) assimilation technique, was developed to obtain reasonable structures of β-mesoscale weather systems by assimilating radar data in a next-generation NWP system named GRAPES (the Global and Regional Assimilation and Prediction System) of China. Single-point testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity (taking Richardson’s equation as the observational operator) can greatly improve the vertical motion. Experiments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further in-depth study.

A PDF-Based Microphysics Parameterization for Simulation of Drizzling Boundary Layer Clouds

Abstract Formulating the contribution of subgrid-scale (SGS) variability to microphysical processes in boundary layer and deep convective cloud parameterizations is a challenging task because of the complexity of microphysical processes and the lack of subgrid-scale information. In this study, a warm-rain microphysics parameterization that is based on a joint double-Gaussian distribution of vertical velocity, liquid water potential temperature, total water mixing ratio, and perturbation of rainwater mixing ratio is developed to simulate drizzling boundary layer clouds with a single column model (SCM). The probability distribution function (PDF) is assumed, but its parameters evolve according to equations that invoke higher-order turbulence closure. These parameters are determined from the first-, second-, and third-order moments and are then used to derive analytical expressions for autoconversion, collection, and evaporation rates. The analytical expressions show that correlation between rainwater and liquid water mixing ratios of the Gaussians enhances the collection rate whereas that between saturation deficit and rainwater mixing ratios of the Gaussians enhances the evaporation rate. Cases of drizzling shallow cumulus and stratocumulus are simulated with large-eddy simulation (LES) and SCM runs (SCM-CNTL and SCM-M): LES explicitly resolves SGS variability, SCM-CNTL parameterizes SGS variability with the PDF-based scheme, but SCM-M uses the grid-mean profiles to calculate the conversion rates of microphysical processes. SCM-CNTL can well reproduce the autoconversion, collection, and evaporation rates from LES. Comparisons between the two SCM experiments showed improvements in mean profiles of potential temperature, total water mixing ratio, liquid water, and cloud amount in the simulations considering SGS variability. A 3-week integration using the PDF-based microphysics scheme indicates that the scheme is stable for long-term simulations.

Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part II: Impact of Polarimetric Data on Storm Analysis

Abstract A data assimilation system based on the ensemble square-root Kalman filter (EnSRF) is extended to include the additional capability of assimilating polarimetric radar variables. It is used to assess the impact of assimilating additional polarimetric observations on convective storm analysis in the Observing System Simulation Experiment (OSSE) framework. The polarimetric variables considered include differential reflectivity ZDR, reflectivity difference Zdp, and specific differential phase KDP. To simulate the observational data more realistically, a new error model is introduced for characterizing the errors of the nonpolarimetric and polarimetric radar variables. The error model includes both correlated and uncorrelated error components for reflectivities at horizontal and vertical polarizations (ZH and ZV, respectively). It is shown that the storm analysis is improved when polarimetric variables are assimilated in addition to ZH or in addition to both ZH and radial velocity Vr. Positive impact is largest when ZDR, Zdp, and KDP are assimilated all together. Improvement is generally larger in vertical velocity, water vapor, and rainwater mixing ratios. The rainwater field benefits the most while the impacts on horizontal wind components and snow mixing ratio are smaller. Improvement is found at all model levels even though the polarimetric data, after the application of thresholds, are mostly limited to the lower levels. Among ZDR, Zdp, and KDP, ZDR is found to produce the largest positive impact on the analysis. It is suggested that ZDR provides more independent information than the other variables. The impact of polarimetric data is also expected to be larger when they are used to retrieve drop size distribution parameters. The polarimetric radar data thresholding prior to assimilation is found to be necessary to minimize the impact of noise. This study is believed to be the first to directly assimilate (simulated) polarimetric data into a numerical model.

An Approach of Radar Reflectivity Data Assimilation and Its Assessment with the Inland QPF of Typhoon Rusa (2002) at Landfall

Abstract A radar reflectivity data assimilation scheme was developed within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) three-dimensional variational data assimilation (3DVAR) system. The model total water mixing ratio was used as a control variable. A warm-rain process, its linear, and its adjoint were incorporated into the system to partition the moisture and hydrometeor increments. The observation operator for radar reflectivity was developed and incorporated into the 3DVAR. With a single reflectivity observation, the multivariate structures of the analysis increments that included cloud water and rainwater mixing ratio increments were examined. Using the onshore Doppler radar data from Jindo, South Korea, the capability of the radar reflectivity assimilation for the landfalling Typhoon Rusa (2002) was assessed. Verifications of inland quantitative precipitation forecasting (QPF) of Typhoon Rusa (2002) showed positive impacts of assimilating radar reflectivity data on the short-range QPF.

Drag-Induced Transfer of Horizontal Momentum between Air and Raindrops

Abstract A theoretical model for unsteady drag-induced transfer of horizontal momentum between air and raindrops in moderate to heavy rainfall is presented. The model accounts for a two-way coupling in which the relative horizontal motion between air and raindrops appears as a drag forcing in both the air and raindrop equations of motion. Analytical solutions of these coupled equations are obtained for the case of rain falling through (i) an initial step change in environmental wind, (ii) a uniform shear profile, and (iii) periodically varying vertical shears of various wavenumbers (a crude proxy for turbulent eddies). Formulas for the propagation (descent) speeds of the shear zones are obtained for (ii), (iii), and for the later stage of (i). However, these speeds are generally quite small—on the order of a few centimeters per second even for heavy rainfall. More importantly, the solutions of (i) and (iii) indicate that the drag interaction leads to a decay of the velocity gradients. A formula for the e-folding decay time of the periodically varying shear profiles indicates that at small wavelengths, the smallest decay times are found for the smaller drops, but at large wavelengths, the smallest decay times are found for the larger drops. The decay times decrease with decreasing wavelength, and approach a value equal to the reciprocal of the product of the rainwater mixing ratio and a drag parameter in the limit of vanishing wavelength. For parameters typical of moderate to heavy rainfall, the small-scale decay times are on the order of a few minutes.