Abstract

We investigated the effect of flow dependency in the assimilation of high-density, high-frequency observations. Radial winds from a Doppler radar are assimilated using a regional hybrid four-dimensional variational data assimilation (4D-Var) scheme with a flow-dependent background error covariance. To consistently assimilate 5 km × 5.625° cell-averaged radial winds at an interval of 10 min, the spatial and temporal correlations of the observation error are statistically diagnosed to be incorporated into the hybrid 4D-Var. The spatial correlation width is larger than that expected from instrument error, suggesting a contribution from representation error whose propagation is also considered to lead to temporal correlation, the width of which is diagnosed to increase with forecast time. The background error covariance also has an important role in incorporating observational information into the analysis. Single observation experiments show that the hybrid 4D-Var has more small-scale structure in its flow-dependent background error correlation than the 4D-Var limited from the climatological background error covariance mainly in the former part of the assimilation window. This suggests the higher potential of the hybrid 4D-Var to allow more higher-wavenumber components in the increment. A case study shows that the hybrid 4D-Var makes better use of the dense and frequent observations, reflecting more detailed representation of flow throughout the assimilation window, leading to promising results in the forecast. Sensitivity experiments also show that it is important to use the optimal observation error correlation. It is suggested that the flow-dependent background error becomes necessary to effectively use high-resolution, high-frequency observations.

Abstract

Himawari-8 optimal cloud analysis (OCA), which employs all 16 channels of the Advanced Himawari Imager, provides cloud properties such as cloud phase, top pressure, optical thickness, effective radius, and water path. By using OCA, the water vapor distribution can be inferred with high spatiotemporal resolution and with a wide coverage, including over the ocean, which can be useful for improving initial states for prediction of the torrential rainfalls that occur frequently in Japan. OCA products were first evaluated by comparing them with different kinds of datasets (surface, sonde, and ceilometer observations) and with model outputs, to determine their data characteristics. Overall, OCA data were consistent with observations of water clouds with moderate optical thicknesses at low to midlevels. Next, pseudorelative humidity data were derived from the OCA products, and utilized in assimilation experiments of a few heavy rainfall cases, conducted with the Japan Meteorological Agency’s nonhydrostatic model–based Variational Data Assimilation System. Assimilation of OCA pseudorelative humidities caused there to be significant differences in the initial conditions of water vapor fields compared to the control, especially where OCA clouds were detected, and their influence lasted relatively long in terms of forecast hours. Impacts of assimilation on other variables, such as wind speed, were also seen. When the OCA data successfully represented low-level inflows from over the ocean, they positively impacted precipitation forecasts at extended forecast times.

Abstract

A cloud-resolving nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) was modified to directly assimilate radar reflectivity and applied to a data assimilation experiment using actual observations of a heavy rainfall event. Modifications included development of an adjoint model of the warm rain process, extension of control variables, and development of an observation operator for radar reflectivity.

The responses of the modified NHM-4DVAR were confirmed by single-observation assimilation experiments for an isolated deep convection, using pseudo-observations of rainwater at the initial and end times of the data assimilation window. The results showed that the intensity of convection could be adjusted by assimilating appropriate observations of rainwater near the convection and that undesirable convection could be suppressed by assimilating small or no reflectivity.

An assimilation experiment using actual observations of a local heavy rainfall in the Tokyo, Japan, metropolitan area was conducted with a horizontal resolution of 2 km. Precipitable water vapor derived from global positioning system data was assimilated at 5-min intervals within 30-min assimilation windows, and surface and wind profiler data were assimilated at 10-min intervals. Doppler radial wind and radar-reflectivity data below the elevation angle of 5.4° were assimilated at 1-min intervals.

The 4DVAR assimilation reproduced a line-shaped rainband with a shape and intensity consistent with the observation. Assimilation of radar-reflectivity data intensified the rainband and suppressed false convection. The simulated rainband lasted for 1 h in the extended forecast and then gradually decayed. Sustaining the low-level convergence produced by northerly winds in the western part of the rainband was key to prolonging the predictability of the convective system.

Abstract

A tornadic supercell and associated low-level mesocyclone (LMC) observed on the Kanto Plain, Japan, on 6 May 2012 were predicted with a nonhydrostatic mesoscale model with a horizontal resolution of 350 m through assimilation of surface meteorological data (horizontal wind, temperature, and relative humidity) of high spatial density and C-band Doppler radar data (radial velocity and rainwater estimated from reflectivity and specific differential phase) with a local ensemble transform Kalman filter. With assimilation of both surface and radar data, a strong LMC was successfully predicted near the path of the actual tornado. When either surface or radar data were not assimilated, however, the LMC was not predicted. Therefore, both surface and radar data were essential for successful LMC forecasts. The factors controlling the strength of the predicted LMC, defined as a low-level maximum vertical vorticity, were clarified by an ensemble-based sensitivity analysis (ESA), which is a new approach for analyzing LMC intensification. The ESA showed that the strength of the LMC was sensitive to low-level convergence forward of the storm and to low-level relative humidity in the rear of the storm. Therefore, the correction of these low-level variables by assimilation of dense observations was found to be particularly important for forecasting and monitoring the LMC in the present case.

Abstract

To identify important factors for supercell tornadogenesis, 33-member ensemble forecasts of the supercell tornado that struck the city of Tsukuba, Japan, on 6 May 2012 were conducted using a mesoscale numerical model with a 50-m horizontal grid. Based on the ensemble forecasts, the sources of the rotation of simulated tornadoes and the relationship between tornadogenesis and mesoscale environmental processes near the tornado were analyzed. Circulation analyses of near-surface, tornadolike vortices simulated in several ensemble members showed that the rotation of the tornadoes could be frictionally generated near the surface. However, the mechanisms responsible for generating circulation were only weakly related to the strength of the tornadoes. To identify the mesoscale processes required for tornadogenesis, mesoscale atmospheric conditions and their correlations with the strength of tornadoes were examined. The results showed that two near-tornado mesoscale factors were important for tornadogenesis: strong low-level mesocyclones (LMCs) at about 1 km above ground level and humid air near the surface. Strong LMCs and large water vapor near the surface strengthened the nonlinear dynamic vertical perturbation pressure gradient force and buoyancy, respectively. These upward forces made contributions essential for tornadogenesis via tilting and stretching of vorticity near the surface.

Abstract

Horizontal convective rolls form in coastal areas around Sendai Airport during sea-breeze events. Using a building-resolving computational fluid dynamics model nested in an advanced forecast system with a data assimilation scheme, the authors perform a series of sensitivity experiments to investigate the impacts of land use and buildings on these rolls. The results show that the roll positions, intensities, and structures are significantly affected by variations in land use and the presence of buildings. Land-use heterogeneity is responsible for generating rolls with evident regional features. Major rolls tend to develop downwind of warm surfaces, and they dominate over neighboring rolls; thus, a heterogeneity-scale mode is imposed on the inherent roll wavelength. The roll’s rapid growth is attributable to warm surfaces that initiate a strong coupling among turbulent thermals, convective updrafts, pressure perturbations, and secondary flows in sea breezes. The heterogeneity-induced features differ considerably from the nearly homogeneous features that form over uniform surfaces. Additionally, the wake flow behind buildings helps organize near-surface warm air into streamwise bands that drive streaky ejections. The building-induced turbulence acts to modify secondary flows and displace roll updrafts toward building wakes. Such effects are most effective over villages with scattered houses that are aligned with the ambient wind. Building signatures are elongated in downwind open areas due to sustained secondary circulations. An analysis of turbulent kinetic energy shows that both land use and buildings regulate energy generation and transport, resulting in a clear response in roll growth. Thus, including complex surfaces in forecast models helps determine detailed characteristics and structures of roll convection over coastal regions.

Abstract

Recently, a humidity estimation technique was developed by using the turbulence echo characteristics detected with a wind-profiling radar. This study is concerned with improvement of the retrieval algorithm for delineating a humidity profile from the refractive index gradient (M) inferred from the echo power. To achieve a more precise estimate of humidity, a one-dimensional variational method is adopted. Because the radar data provide only the absolute value of M, its sign must be determined in the retrieval. A statistical probability for the sign of M [Pr(z)] is introduced to the cost function of the variational method to determine the optimum result with reduced calculation cost. GPS-derived integrated water vapor (IWV) was assimilated together with the radar-derived |M| for constraining the signs of |M| to agree with the radar-derived IWV and the GPS-derived IWV. Humidity profiles were retrieved from the Middle and Upper Atmosphere (MU) radar–Radio Acoustic Sounding System (RASS) data for July–August 1999 using the first guess calculated from the time interpolation of radiosonde results. The |M| profiles from the MU radar–RASS were assimilated at 21 height layers between 1.5 and 7.5 km. A genetic algorithm is employed to find the global optimum. The humidity profiles are retrieved with the same vertical resolution as that of the observation values. The precision of the retrieval result using the new method is superior to that of the conventional method. The difference between the analysis and simultaneous radiosonde results was related to a large error in the first guess. The sensitivity of the analysis result to the shape of the Pr(z) profile was investigated, and the result appears to be insensitive to the profile of Pr(z). The improvement over the conventional method is especially evident for the case of a large error in the first guess.

Abstract

The authors evaluated the effects of assimilating three-dimensional Doppler wind lidar (DWL) data on the forecast of the heavy rainfall event of 5 July 2010 in Japan, produced by an isolated mesoscale convective system (MCS) at a meso-gamma scale in a system consisting of only warm rain clouds. Several impact experiments using the nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) and the Japan Meteorological Agency nonhydrostatic model with a 2-km horizontal grid spacing were conducted in which 1) no observations were assimilated (NODA), 2) radar reflectivity and radial velocity determined by Doppler radar and precipitable water vapor determined by GPS satellite observations were assimilated (CTL), and 3) radial velocity determined by DWL were added to the CTL experiment (LDR) and five data denial and two observational error sensitivity experiments. Although both NODA and CTL simulated an MCS, only LDR captured the intensity, location, and horizontal scale of the observed MCS. Assimilating DWL data improved the wind direction and speed of low-level airflows, thus improving the accuracy of the simulated water vapor flux. The examination of the impacts of specific assimilations and assigned observation errors showed that assimilation of all data types is important for forecasting intense MCSs. The investigation of the MCS structure showed that large amounts of water vapor were supplied to the rainfall event by southerly flow. A midlevel inversion layer led to the production of exclusively liquid water particles in the MCS, and in combination with the humid airflow into the MCS, this inversion layer may be another important factor in its development.

Abstract

Horizontal convective rolls (HCRs) that develop in sea breezes greatly influence local weather in coastal areas. In this study, the authors present a realistic simulation of sea-breeze HCRs over an urban-scale area at a resolution of a few meters. An advanced Down-Scaling Simulation System (DS³) is built to derive the analyzed data using a nonhydrostatic model and data assimilation scheme that drive a building-resolving computational fluid dynamics (CFD) model. The mesoscale-analyzed data well capture the inland penetration of the sea breeze in northeastern Japan. The CFD model reproduces the HCRs over Sendai Airport in terms of their coastal initiation, inland growth, streamwise orientation, specific locations, roll wavelength, secondary flows, and regional differences due to complex surfaces. The simulated HCRs agree fairly well with those observed by dual-Doppler lidar and heliborne sensors. Both the simulation and observation analyses suggest that roll updrafts typically originate in the narrow bands of low-speed streaks and warm air near the ground. The HCRs are primarily driven and sustained by a combination of wind shear and buoyancy forces within the slightly unstable sea-breeze layer. In contrast, the experiment without data assimilation exhibits a higher deficiency in the reproduction of roll characteristics. The findings highlight that CFD modeling, given reliable mesoscale weather and surface conditions, aids in high-precision forecasting of HCRs at unprecedented high resolutions, which may help determine the roll structure, dynamics, and impacts on local weather.

Abstract

We conducted an observational survey using a ground-based water vapor Raman lidar (RL) during the warm season in Japan to investigate the water vapor structure of low-level inflows that contribute to the formation of a mesoscale convective system (MCS). After the passage of a warm front, low-level moisture convergence contributed to the initiation and development of numerous convective clouds that composed the MCS. The RL observations showed that the vertical profiles of the water vapor mixing ratio (WVMR) associated with low-level inflows into the MCS exceeded 20 g kg⁻¹ below 500 m above sea level, which is comparable to WVMRs in previous reports associated with MCSs in Japan and the United States. We conducted two assimilation experiments using a four-dimensional variational data assimilation system: one is to assimilate operational observational data (CNTL), and the other is to assimilate WVMR vertical profiles and operational observational data (TEST). A comparison between TEST and CNTL showed that data assimilation of the WVMR vertical profiles not only modified the moisture field but also the wind field. It appears that the modifications observed in horizontal wind are related to the modification of the WVMR in the analysis fields. These WVMR and wind modifications improved the reproduction of the frontal surface and forecasting of 6-h precipitation amount slightly. Data assimilation of vertical profiles of the WVMR has positive and negative impacts on the WVMR and horizontal wind, respectively, implying that the vertical profiles of both the horizontal wind and the WVMR might better estimate initial conditions and forecasts.