The objective of this study is to improve forecast accuracy by using low-precision floating-point arithmetic when performing ensemble weather forecasting. Low-precision floating-point arithmetic is reproduced using a software emulator developed to allow the mantissa bit length of floating-point numbers to be adjusted in one-bit increments. First, two different methods of generating an ensemble forecast using low-precision techniques were compared with a conventional ensemble-generation approach. For one, the precision of the initial conditions is reduced (called initial value ensemble), and for the other, the precision of the model calculations is reduced (called model ensemble). Then, it is found that the former technique is inadequate for generating sufficient ensemble spread, but the latter gives an ensemble spread comparable to the reference. In order to further evaluate the ensemble method using low-precision floating-point arithmetic in accordance with the model ensemble method, ensemble forecasting experiments were conducted in combination with the conventional ensemble method. As a result, the combined ensemble forecast had a higher spread evaluation index than the ensemble forecast using only the low-precision floating-point arithmetic and the conventional ensemble method. The reasons why the ensemble forecasts have higher index when incorporating low-precision floating-point ensemble methods are considered as follows: weather forecast models do not reproduce weather phenomena below the grid scale due to their low spatio-temporal resolution, and some models incorporate statistical assumptions to reduce computational load, which suppress the random nature of weather phenomena rather than actual weather events. On the other hand, ensemble methods using low-precision floating-point arithmetic can compensate for this randomness, and thus are expected to have higher evaluation index. This suggests that low-precision floating-point arithmetic, implemented in hardware by using Field Programmable Gate-Arrays (FPGAs) for example, may allow for faster operations without compromising forecast accuracy in ensemble forecasting.

In this study, our objective is to identify the appropriate cold pool scales over Taiwan’s complex topography during predominant afternoon thunderstorms under a local-circulation dominated weather regime in summer. We utilize semi-realistic TaiwanVVM simulations, which cover the entire area of Taiwan, to investigate this phenomenon. Our findings reveal that when buoyancy is defined using a conventional environmental scale (109 km), the cold pool locations do not align with the precipitation areas, instead being concentrated mainly along the mountain ridges. We hypothesize that this discrepancy arises from the environmental scale at which cold pool buoyancy operates. To assess this, we conducted systematic analyses and the results show that an optimal environmental scale of approximately 7 km to 11 km (about 3 times of the 75th and 90th percentile of the precipitation object length) can be identified. The statistics of cold pool frequency better align with precipitation hotspots, characterized by evaporative cooling over the plains and increased water loading within the core of precipitation objects over the mountains. We demonstrate that this method effectively captures the shift in cold pools associated with precipitation responses in a warming climate in Taiwan. This work highlights the importance of using an appropriate environmental scale when estimating buoyancy over complex topography.

This study investigates the microphysical characteristics of warm-season precipitation with observations from the second generation Parsivel disdrometer OTT2 in Ningbo, situated in eastern coastal China. A comparative analysis is conducted on the raindrop size distribution (DSD) across various rain types and regions, with a focus on elucidating the relationships between different rain rate (R), raindrop sizes, concentrations, and radar reflectivity (Z). Moreover, this study meticulously analyzes the shape-slope (μ−Λ) relationship of raindrops during the warm season in this region. The results reveal that during warm-season convection in coastal eastern China, the mass-weighted mean diameter (D_m) and the logarithmic generalized intercept parameter (log₁₀ N_w) are 2.21 mm and 3.51, respectively. This indicates the presence of low-concentration large raindrops, distinguishing this region from other parts of China such as Guangdong, Hubei, Nanjing, and Beijing. Additionally, the enhancement of convective R is predominantly driven by the increase in raindrop size. Convective rainfall accounts for 67.0 % of the total precipitation, while stratiform contributes 11.1 %. Both types of rain display a unimodal distribution in number concentration and diameter, peaking at 0.3–0.6 mm. Additionally, both generally follow the three-parameter Gamma distribution, despite minor deviations in the occurrences of larger and smaller raindrops. The μ−Λ relationship in eastern coastal China is similar to that of the southern coastal regions, both being dominated by large raindrops. The Z–R relationship for warm-season convection is expressed as Z = 396.96R^1.34. These findings are vital for optimizing regional model cloud microphysics parameterization and improving the precision of local radar-based quantitative precipitation estimates.

Flood early warning systems are crucial for mitigating flood damage; however, limitations in forecasting technology lead to false alarms and missed events in warnings. Repeated occurrences of these issues may cause people to hesitate to take appropriate action during subsequent warnings, potentially exacerbating flood damage. However, the effects of warning performance on flood damage in Japan have not been analyzed for actual flood events. This study empirically examined these effects by applying Bayesian regression analyses to open data on the 2018 Japan Floods in 127 municipalities in four prefectures (i.e., Okayama, Hiroshima, Ehime, and Fukuoka) for which data were available on the real-time flood warning map (Kouzui Kikikuru in Japanese) during the 2018 Japan Floods, which provides limited open data on warning performance. Based on these data, the false alarm ratio (FAR) and missed event ratio (MER) for each municipality before the 2018 Japan Floods were calculated and used as explanatory variables. The (1) fatalities, (2) injuries, (3) economic losses to general assets, and (4) economic losses to crops during the 2018 Japan Floods were used as outcome variables. The results indicate that a higher FAR was associated with an increase in fatalities, injuries, and economic losses to general assets. By contrast, no prominent positive effect of MER was found for any outcome variable. Although our results are fundamental, they provide valuable insights for improving warning systems and guiding future research.

This paper presents a method for estimating turbulent fluxes using the Bowen ratio method. We propose a data exclusion criterion for the Bowen ratio method, defining an exclusion range for the Bowen ratios (B) as B₋ < B < B₊. To determine B₋ and B₊, we evaluated the flux difference between the Bowen ratio method and the eddy covariance method and suggested B₋ = −2.0 and B₊ = −0.6. We also propose an interpolation method to handle missing data based on the exclusion criteria by interpolating B/(1 + B) or 1 /(1 + B). By implementing these approaches, we establish a comprehensive and practical framework for estimating turbulent fluxes with the Bowen ratio method. We expect that these results will enhance the accuracy and reliability of flux estimates in various environmental and climate studies.