Global Precipitation Map using Satelliteborne Microwave Radiometers by the GSMaP Project : Production and Validation

Kubota, Takuji; Hashizume,; Takahashi, Daisuke; Shige,; Okamoto,; Ushio,; Aonashi,; Kachi,

doi:10.1109/igarss.2006.668

Cited by 50 publications

(43 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…With the availability of high-resolution remotely sensed data, advanced algorithms for retrieving rainfall from satellite-based microwave and infrared observations have been developed (Sorooshian et al, 2000;Huffman et al, 2002;Kidd et al, 2003;Joyce et al, 2004;Kubota et al, 2007). Consequently, a number of general-use satellite-based precipitation products have emerged (e.g.…”

Section: Quantitative Precipitation Estimates (Qpes)mentioning

confidence: 99%

A review of advances in flash flood forecasting

Hapuarachchi

Wang

Pagano

2011

Hydrological Processes

410

286

View full text Add to dashboard Cite

Abstract:Flash flooding is one of the most hazardous natural events, and it is frequently responsible for loss of life and severe damage to infrastructure and the environment. Research into the use of new modelling techniques and data types in flash flood forecasting has increased over the past decade, and this paper presents a review of recent advances that have emerged from this research. In particular, we focus on the use of quantitative precipitation estimates and forecasts, the use of remotely sensed data in hydrological modelling, developments in forecasting models and techniques, and uncertainty estimates. Over the past decade flash flood forecast lead-time has expanded up to six hours due to improved rainfall forecasts. However the largest source of uncertainty of flash flood forecasts remains unknown future precipitation. An increased number of physically based hydrological models have been developed and used for flash flood forecasting and they have been found to give more plausible results when compared with the results of conceptual, statistical, and neural network models. Among the three methods for deciding flash flood occurrence discussed in this review, the rainfall comparison method (flash flood guidance) is most commonly used for flash flood forecasting as it is easily understood by the general public. Unfortunately, no existing model is capable of making reliable flash flood forecasts in urban watersheds even though the incidence of urban flash flooding is increasing due to increasing urbanisation.

show abstract

Section: Quantitative Precipitation Estimates (Qpes)mentioning

confidence: 99%

A review of advances in flash flood forecasting

Hapuarachchi

Wang

Pagano

2011

Hydrological Processes

410

286

View full text Add to dashboard Cite

show abstract

“…Mapping of Precipitation (aka GSMaP_Gauge; Kubota et al, 2007, Ushio et al, 2009, and conventional observation from the National Centers for Environmental Prediction (NCEP; aka NCEP PREPBUFR). See Terasaki et al (2015), Terasaki and Miyoshi (2017), and for details regarding how PREPBUFR, AMSU-A radiance, and GSMaP_Gauge data are assimilated in the NICAM-LETKF.…”

Section: Observation Data For Model State Estimationsmentioning

confidence: 99%

Data Assimilation for Climate Research: Model Parameter Estimation of Large‐Scale Condensation Scheme

2020

View full text Add to dashboard Cite

This study proposes using data assimilation (DA) for climate research as a tool for optimizing model parameters objectively. Mitigating radiation bias is very important for climate change assessments with general circulation models. With the Nonhydrostatic ICosahedral Atmospheric Model (NICAM), this study estimated an autoconversion parameter in a large-scale condensation scheme. We investigated two approaches to reducing radiation bias: examining useful satellite observations for parameter estimation and exploring the advantages of estimating spatially varying parameters. The parameter estimation accelerated autoconversion speed when we used liquid water path, outgoing longwave radiation, or outgoing shortwave radiation (OSR). Accelerated autoconversion reduced clouds and mitigated overestimated OSR bias of the NICAM. An ensemble-based DA with horizontal localization can estimate spatially varying parameters. When liquid water path was used, the local parameter estimation resulted in better cloud representations and improved OSR bias in regions where shallow clouds are dominant.

show abstract

“…Due to the relatively low model top height (approximately 40 km), the present NICAM-LETKF assimilates only three channels (6−8) of the AMSU-A radiances and excludes channels that are sensitive to the stratosphere and mesosphere (Terasaki and Miyoshi, 2017). The GSMaP is global surface precipitation data estimated by passive microwave radiometers (Kubota et al, 2007;Ushio et al, 2009). This study assimilates a near-real-time version of the GSMaP data with the Gaussian transformation (GT) method (Lien et al, 2013;2016a;2016b;Kotsuki et al, 2017b).…”

Section: Nicam-letkf Systemmentioning

confidence: 99%

On the properties of ensemble forecast sensitivity to observations

Kotsuki

Kurosawa

Miyoshi

2019

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

Evaluating impacts of observations on the skill of numerical weather prediction (NWP) is important. The Ensemble Forecast Sensitivity to Observation (EFSO) provides an efficient approach to diagnosing observation impacts, quantifying how much each observation improves or degrades a subsequent forecast with a given verification reference. This study investigates the sensitivity of EFSO impact estimates to the choice of the verification reference, using a global NWP system consisting of the Non‐hydrostatic Icosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF). The EFSO evaluates observation impacts with the moist total energy norm and with recently proposed observation‐based verification metrics. The results show that each type of observation mainly contributes to the improvement of forecast departures of the observed variable maybe due to the limitation of localization in the EFSO. The EFSO overestimates the fraction of beneficial observations when verified with subsequent analyses, especially for shorter lead times such as 6 h. We may avoid this overestimation to some extent by verifying with observations, analyses from other data assimilation (DA) systems, or analyses of an independent run with the same DA system. In addition, this study demonstrates two important issues possibly leading to overestimating observation impacts. First, observation impacts would be overestimated if we apply relaxation‐to‐prior methods to the initial conditions of the ensemble forecasts in the EFSO; therefore, the ensemble forecasts in the EFSO should be independent of the ensemble forecasts in the DA cycle. Second, deterministic baseline forecasts of the EFSO, which represent the forecast without DA, should be initialized by the ensemble mean of the first guess at the analysis time, not by the previous analysis.

show abstract

Global Precipitation Map using Satelliteborne Microwave Radiometers by the GSMaP Project : Production and Validation

Cited by 50 publications

References 12 publications

A review of advances in flash flood forecasting

A review of advances in flash flood forecasting

Data Assimilation for Climate Research: Model Parameter Estimation of Large‐Scale Condensation Scheme

On the properties of ensemble forecast sensitivity to observations

Contact Info

Product

Resources

About