Global Precipitation Forecasts by Merging Extrapolation-Based Nowcast and Numerical Weather Prediction with Locally Optimized Weights

Kotsuki, Shunji; Kurosawa, Kenta; Otsuka, Shigenori; Terasaki, Koji; Miyoshi, Takemasa

doi:10.1175/waf-d-18-0164.1

Cited by 18 publications

(17 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we investigate a series of experiments for optimizing localization scales (Appendix A). To investigate the optimal localization scale spatially, we evaluate the first‐guess accuracy locally (Kotsuki et al ., 2019b). We introduce the local RMSE, defined by

{(italiclocal italicRMSE)}_{i} = \sqrt{\frac{\sum_{t} \sum_{j \in D_{i}} {false({\overset{x}{true}}_{j, t}^{f} - {boldx}_{j, t}^{italictrue} false)}^{2} \cdot S_{j}}{\sum_{t} \sum_{j \in D_{i}} S_{j}}},

where D i is the local domain of the grid point i and is set as the surrounding grids within 1,000 km in this study.…”

Section: Discussionmentioning

confidence: 99%

Weight structure of the Local Ensemble Transform Kalman Filter: A case with an intermediate atmospheric general circulation model

Kotsuki

Pensoneault

Okazaki

et al. 2020

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

The Local Ensemble Transform Kalman Filter (LETKF) computes analysis by using a weighted average of the first‐guess ensemble with surrounding observations within a localization cut‐off radius. Since overlapped observations are assimilated at neighbouring grid points, the LETKF results in spatially smooth weights. This study explores the spatial structure of the weights with the intermediate atmospheric model SPEEDY (Simplified Parameterizations, Primitive Equation Dynamics). Based on the characteristics of the weight structure, we also aim to improve the weight interpolation (WI) method, which we use to compute the weights at coarser reference points and interpolate the weights into higher‐resolution model grid points. The results show that larger localization and sparser observations result in spatially smoother weights. WI is less detrimental when weight patterns are spatially smoother. An advanced WI method with observation‐density‐dependent reference points results in better forecasts than those with uniformly distributed reference points. This improvement may be due to the spatially inhomogeneous localization function realized by the WI method with observation‐density‐dependent reference points. The spatial distribution of the optimal localization scales shows that larger (smaller) localization is beneficial in sparsely (densely) observed regions. The WI method is computationally more efficient with larger ensembles since the additional computational cost for the WI is lower than that for the LETKF.

show abstract

{(italiclocal italicRMSE)}_{i} = \sqrt{\frac{\sum_{t} \sum_{j \in D_{i}} {false({\overset{x}{true}}_{j, t}^{f} - {boldx}_{j, t}^{italictrue} false)}^{2} \cdot S_{j}}{\sum_{t} \sum_{j \in D_{i}} S_{j}}},

where D i is the local domain of the grid point i and is set as the surrounding grids within 1,000 km in this study.…”

Section: Discussionmentioning

confidence: 99%

Weight structure of the Local Ensemble Transform Kalman Filter: A case with an intermediate atmospheric general circulation model

Kotsuki

Pensoneault

Okazaki

et al. 2020

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

show abstract

“…On the contrary, weights for NWP model forecasts increase with the increase of lead times. The weighting of forecasts is not trivial and is usually derived using historical data [121]). A well-known forecasting tool to the nowcasting of heavy precipitation is represented by the model NIMROD (Golding, 1998), which utilizes both the extrapolation of an existing precipitating field, which is derived from satellite and radar data, and precipitation forecasts of a NWP model.…”

Section: Blending Methodsmentioning

confidence: 99%

The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review

et al. 2021

View full text Add to dashboard Cite

Radar-based rainfall information has been widely used in hydrological and meteorological applications, as it provides data with a high spatial and temporal resolution that improve rainfall representation. However, the broad diversity of studies makes it difficult to gather a condensed overview of the usefulness and limitations of radar technology and its application in particular situations. In this paper, a comprehensive review through a categorization of radar-related topics aims to provide a general picture of the current state of radar research. First, the importance and impact of the high temporal resolution of weather radar is discussed, followed by the description of quantitative precipitation estimation strategies. Afterwards, the use of radar data in rainfall nowcasting as well as its role in preparation of initial conditions for numerical weather predictions by assimilation is reviewed. Furthermore, the value of radar data in rainfall-runoff models with a focus on flash flood forecasting is documented. Finally, based on this review, conclusions of the most relevant challenges that need to be addressed and recommendations for further research are presented. This review paper supports the exploitation of radar data in its full capacity by providing key insights regarding the possibilities of including radar data in hydrological and meteorological applications.

show abstract

“…Many leading weather forecasting centres now have nowcasting systems that seamlessly blend extrapolated observations with numerical weather prediction (NWP) output, with an increasing dependence on the latter for longer lead times (Sun et al ., 2014; Kotsuki et al ., 2019; Nerini et al ., 2019). A direct comparison between NWP and nowcasting skill for tropical Africa would be useful not only in this context but also for forecasters who need to try to combine both these sources of information optimally.…”

Section: Discussionmentioning

confidence: 99%

How skilful are Nowcasting Satellite Applications Facility products for tropical Africa?

Hill

Stein

Roberts

et al. 2020

Meteorological Applications

View full text Add to dashboard Cite

Satellite nowcasting potentially provides a vital opportunity to mitigate against the risks of severe weather in tropical Africa, where population growth and climate change are exposing an ever growing number of people to weather hazards. Numerical weather prediction demonstrates limited skill for much of Africa and weather radars are rare. However, geostationary satellites provide excellent spatial and temporal coverage of the often long-lasting convective storms that deliver heavy rain, lightning and strong winds, presenting a valuable opportunity for satellite nowcasting. Here, we evaluate the skill of satellite nowcasting products for tropical Africa: these products are routinely generated, but to our best knowledge never routinely used in tropical Africa before the Global Challenges Research Fund African SWIFT (Science for Weather Information and Forecasting Techniques) project. Focusing in particular on convective rainfall rate (CRR) and rapidly developing thunderstorm convection warning (RDT-CW) products, we demonstrate that both are useful nowcasting tools. The CRR product produces very different rainfall climatologies for day and night in tropical Africa. This is associated with greater skill of the product during daytime, particularly for heavier rain rates. The RDT-CW product is able to identify around 60% of heavy (>5 mmÁhr −1) rainfall events with the fraction detected increasing with increasing rainfall rate. For both products, extrapolation forwards in time (up to 90 and 60 min, respectively) maintains useful skill in tropical Africa, motivating work to develop longer lead-time nowcasts. We conclude that widespread uptake of satellite nowcasting could provide new skilful weather predictions on short timescales in much of tropical Africa.

show abstract

Global Precipitation Forecasts by Merging Extrapolation-Based Nowcast and Numerical Weather Prediction with Locally Optimized Weights

Cited by 18 publications

References 26 publications

Weight structure of the Local Ensemble Transform Kalman Filter: A case with an intermediate atmospheric general circulation model

Weight structure of the Local Ensemble Transform Kalman Filter: A case with an intermediate atmospheric general circulation model

The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review

How skilful are Nowcasting Satellite Applications Facility products for tropical Africa?

Contact Info

Product

Resources

About