The complex relationships between rainfall amounts and their causes require further clarification through analytical research. This study utilizes ensemble‐based singular value decomposition (ESVD) ...analysis that decomposes the ensemble‐based cross‐covariance matrix between datasets related to atmospheric states and hydrometeors. ESVD analysis is applied to the “Heavy Rain Event of July 2018 in Japan.” The initial states of 301‐member ensemble forecasts are created using a local ensemble transform Kalman filter, and the ensemble forecasts are obtained by the regional nonhydrostatic model (2 km horizontal grid interval). The ESVD analysis results indicate that the heterogeneous correlation maps of the first mode (maximum squared singular value) and second mode exhibit high correlations between the synoptic‐scale atmospheric states (the location of the stationary front and the baroclinically enhanced updraft) and rainfall characteristics. The results obtained using the sixth mode show that local water vapor fluxes in the lower layer are correlated with mesoscale rainfall characteristics. Therefore, ESVD analysis can be used to clarify multiple, independent relationships between multiscale atmospheric states and heavy rainfall.
This study proposed ensemble‐based singular value decomposition (ESVD) analysis, which calculates the singular values and correlation maps of the ensemble‐based cross‐covariance matrices. The ESVD analysis can be used to clarify multiple, independent relationships between two types of variables associated with a specific rainfall event such as multiscale atmospheric states and hydrometeors.
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.
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
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.
Experiments with two ensemble systems of resolutions 10 km (MF10km) and 2 km (MF2km) were designed to examine the value of cloud-resolving ensemble forecast in predicting precipitation on small ...spatio-temporal scales. Since the verification was performed on short-term precipitation at high resolution, uncertainties from small-scale processes caused the traditional verification methods to be inconsistent with the subjective evaluation. An extended verification method based on the Fractions Skill Score (FSS) was introduced to account for these uncertainties. The main idea is to extend the concept of spatial neighbourhood in FSS to the time and ensemble dimension. The extension was carried out by recognising that even if ensemble forecast is used, small-scale variability still exists in forecasts and influences verification results. In addition to FSS, the neighbourhood concept was also incorporated into reliability diagrams and relative operating characteristics to verify the reliability and resolution of two systems.
The extension of FSS in time dimension demonstrates the important role of temporal scales in short-term precipitation verification at small spatial scales. The extension of FSS in ensemble space is called the ensemble FSS, which is a good representative of FSS for ensemble forecast in comparison with the FSS of ensemble mean. The verification results show that MF2km outperforms MF10km in heavy rain forecasts. In contrast, MF10km was slightly better than MF2km in predicting light rains, suggesting that the horizontal resolution of 2 km is not necessarily enough to completely resolve convective cells.
The feasibility of regional reanalysis assimilating only conventional observations was investigated as an alternative to dynamical downscaling to estimate the past three-dimensional high-resolution ...atmospheric fields with long-term homogeneity over about 60 years. The two types of widely applied dynamical downscaling approaches have problems. One, with a serial long-term time-integration, often fails to reproduce synoptic-scale systems and precipitation patterns. The other, with frequent reinitializations, underestimates precipitation due to insufficient spin-up. To address these problems maintaining long-term homogeneity, we proposed the regional reanalysis assimilating only the conventional observations. We examined it by paying special attention to summer precipitation, through one-month experiment before conducting a long-term reanalysis. The system was designed to assimilate surface pressure and radiosonde upper-air observations using the Japan Meteorological Agency's nonhydrostatic model (NHM) and the local ensemble transform Kalman filter (LETKF). It covered Japan and its surrounding area with a 5-km grid spacing and East Asia with a 25-km grid spacing, applying one-way double nesting in the Japanese 55-year reanalysis (JRA-55). The regional reanalysis overcame the problems with both types of dynamical downscaling approaches. It reproduced actual synoptic-scale systems and precipitation patterns better. It also realistically described spatial variability and precipitation intensity. The 5-km grid spacing regional reanalysis reproduced frequency of heavy precipitation and described anomalous local fields affected by topography, such as circulations and solar radiation, better than the coarser reanalyses. We optimized the NHM-LETKF for long-term reanalysis by sensitivity experiments. The lateral boundary perturbations that were derived from an empirical orthogonal function analysis of JRA-55 brought stable analysis, saving computational costs. The ensemble size of at least 30 was needed, because further reduction significantly degraded the analysis. The deterministic run from non-perturbed analysis was adopted as a first guess in LETKF instead of the ensemble mean of perturbed runs, enabling reasonable simulation of spatial variability in the atmosphere and precipitation intensity.
Rapid scan atmospheric motion vectors (RS-AMVs) were derived using an algorithm developed by the Meteorological Satellite Center of the Japan Meteorological Agency (JMA) from Himawari-8 rapid scan ...imagery over the area around Japan. They were computed every 10 min for seven different channels, namely, the visible channel (VIS), near infrared and infrared channels (IR), three water vapor absorption channels (WV), and CO2 absorption channel (CO2), from image triplets with time intervals of 2.5 min for VIS and 5 min for the other six channels. In June 2016, the amount of data was increased by more than 20 times compared to the number of routinely used AMVs. To exploit these high-resolution data in mesoscale data assimilation for the improvement of short-range forecasts, data verification, and assimilation experiments were conducted. The RS-AMVs were of sufficiently good quality for assimilation and consistent overall with winds from JMA's mesoscale analyses, radiosonde, and wind profiler observations. Errors were slightly larger in WV than in VIS and IR channels. Significant negative biases relative to sonde winds were seen at high levels in VIS, IR, and CO2, whereas slightly positive biases were noticeable in WV at mid- to high levels. Data assimilation experiments with the JMA's non-hydrostatic model based Variational Data Assimilation System (JNoVA) on a cold vortex event in June 2016 were conducted using RS-AMVs from seven channels. The wind forecasts improved slightly in early forecast hours before 12 hours in northern Japan, over which the vortex passed during the assimilation period. They also showed small improvements at low levels when averaged over the whole forecast period. The results varied slightly depending on the channels used for assimilation, which might be caused by different error characteristics of RS-AMVs in different channels.
Sea‐breeze front (SBF) can cause dramatic changes in weather and air quality near the coast. However, the observation and forecast of its three‐dimensional (3‐D) fine‐scale structures have been ...challenging. Using mesoscale‐to‐large eddy simulations (LES) models and high‐resolution lidar measurement over Sendai Airport, here we perform a successful simulation of the observed 3‐D structures of an SBF for the first time. We show that frontal structures are characterized by a series of lobes (spaced ~500 m apart) aligned along the raised sea‐breeze head, where the shear between sea breeze and alongshore ambient flow aloft is evident. Local strong updrafts occur both in the frontal lobes of marine cold air and in the prefrontal warm air ascending the wedge of windward lobes. Downdrafts form behind the lifted marine cold air and trap air pollutants. These fine‐scale structures and vertical motions are repeatedly strengthened by the short‐term disturbances of gravity currents that move onshore and collide with the SBF. They are also affected by buildings and determine the detailed variations of surface winds. We conclude that advanced observation and modeling systems can potentially improve the prediction of coastal weather and environment.
Plain Language Summary
When sea breeze comes, it does not come gently and often brings a sudden change in winds, temperature, and air quality. The so‐called sea‐breeze front has great influence on the environment in coastal areas around the world. This work presents a major progress to reveal its fine‐scale 3‐D structures using the state‐of‐art observations and numerical models. The dynamics and evolution of the frontal structures are further linked to the disturbances of gravity current and the effect of buildings near the coast. Some differences to the known concept of idealized sea‐breeze front are also identified. We believe that the findings have significant impacts on the research community of weather forecast, numerical modeling, and coastal environment studies.
Key Points
Lidar observations capture well the fine‐scale 3‐D structures and evolution of a typical sea‐breeze front
A novel local prediction system can reproduce frontal lobes/clefts and updrafts at high accuracy and resolution
Gravity current disturbances control the short‐term variations of frontal structures, while coastal buildings affect detailed features
Considering urbanization effects on atmospheric states and subsequent precipitation is crucial to improve the accuracy of forecasting localized heavy rainfall around urban areas and to mitigate ...related disasters. For this purpose, it is effective to use a time development model that can accurately represent city-specific effects, such as urban heat island effect, in the assimilation process, and to assimilate high-frequency/high-density surface observation data that have not been used thus far. Therefore, this study incorporated a forecast model with an urban canopy scheme into an ensemble-based assimilation system and assimilated dense surface data from an Atmospheric Environmental Regional Observation System. Then, we performed analysis-forecast experiments for a heavy rain event in Tokyo metropolitan area on 30 August 2017, to examine the impact of urbanization. Our results showed that the urban scheme and surface observation improved near-surface temperature and moisture fields, thereby contributing to the formation of a clearer convergence line between the easterly and southerly winds where it was observed. Consequently, these improvements resulted in an earlier onset of rainfall and better reproduction of the heavy rainfall distribution.
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