A comparison of the rainfall forecasting skills of the WRF ensemble forecasting system using SPCPT and other cumulus parameterization error representation schemes

Wu, Tianjie; Min, Jinzhong; Wu, Shu Hui

doi:10.1016/j.atmosres.2018.11.016

Cited by 9 publications

(8 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main parameterization options are shown in Table 2. The scheme of SPPT is on the basis of the assumption that the parameterized physics tendencies have uncertainties [28,30,37], as mentioned in Section 1. Therefore, a presentation of model uncertainty was prepared by perturbing the parameterized accumulated physical tendencies of four different variables, wind tendency (u and v), temperature tendency (T), and relative humidity tendency (R q ), at each time step.…”

Section: Ensemble Forecasting With Nwp Model 231 Wrf Modelmentioning

confidence: 99%

“…Some researchers have made efforts to take advantage of the stochastic total tendency perturbation (STTP) scheme, which represents the uncertainty concerning both the physics and the dynamics in a single model. Some studies have utilized the scheme of stochastic physic perturbation tendency (SPPT), where the uncertainty is related to the total model physical process [28]. In addition, another approach has been used to present the uncertainty in the stochastic kinetic energy backscatter (SKEB) scheme [29].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

Dou

Chai

2021

Remote Sensing

View full text Add to dashboard Cite

The interferometric synthetic aperture radar (InSAR) technique is widely utilized to measure ground-surface displacement. One of the main limitations of the measurements is the atmospheric phase delay effects. For satellites with shorter wavelengths, the atmospheric delay mainly consists of the tropospheric delay influenced by temperature, pressure, and water vapor. Tropospheric delay can be calculated using numerical weather prediction (NWP) model at the same moment as synthetic aperture radar (SAR) acquisition. Scientific researchers mainly use ensemble forecasting to produce better forecasts and analyze the uncertainties caused by physic parameterizations. In this study, we simulated the relevant meteorological parameters using the ensemble scheme of the stochastic physic perturbation tendency (SPPT) based on the weather research forecasting (WRF) model, which is one of the most broadly used NWP models. We selected an area in Foshan, Guangdong Province, in the southeast of China, and calculated the corresponding atmospheric delay. InSAR images were computed through data from the Sentinel-1A satellite and mitigated by the ensemble mean of the WRF-SPPT results. The WRF-SPPT method improves the mitigating effect more than WRF simulation without ensemble forecasting. The atmospherically corrected InSAR phases were used in the stacking process to estimate the linear deformation rate in the experimental area. The root mean square errors (RMSE) of the deformation rate without correction, with WRF-only correction, and with WRF-SPPT correction were calculated, indicating that ensemble forecasting can significantly reduce the atmospheric delay in stacking. In addition, the ensemble forecasting based on a combination of initial uncertainties and stochastic physic perturbation tendencies showed better correction performance compared with the ensemble forecasting generated by a set of perturbed initial conditions without considering the model’s uncertainties.

show abstract

Section: Ensemble Forecasting With Nwp Model 231 Wrf Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

Dou

Chai

2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…This approach demonstrates model abilities, but also provides a testbed for feedback and model improvement. Two key examples of this approach are in weather prediction (Bengtsson et al 2019;Wu et al 2019) and fishery stock assessment (GMFMC 2018). Future directions in model development for predicting ES should also include identification of such opportunities to engage in short-term forecasting in partnership with decision makers.…”

Section: Connecting Science and Policy Objectives In Modelsmentioning

confidence: 99%

Mathematical Modeling for Ecosystem-Based Management (EBM) and Ecosystem Goods and Services (EGS) Assessment

Fulford

Heymans²,

2020

Ecosystem-Based Management, Ecosystem Services and Aquatic Biodiversity

View full text Add to dashboard Cite

There is a rich history in predicting ecological interactions in nature going back to the seminal work of Robert May. Historically, the use of models in ecological decision making has been centered on tools that require less data and are easier to communicate into policy. However, the explosion in the availability of ecological data, as well as the ready access to computer power, has opened the door to more detailed computation tools. This in turn has created a suite of questions about how and how much model-based projections can influence decision making in management of ecological resources. Here we address some of these issues particularly those created by model complexity, data quality and availability, and model acceptance by policy makers. Our goal is to use specific examples to critically examine these issues for both gaps and opportunities in how models can be used to inform decision making at the level of ecosystems and in the currency of ecosystem services to people.

show abstract

“…Changes in rainfall patterns affect many sectors of a country such as agriculture, economy, and disaster management [15]. Based on this evidence, the researchers are more and more interested in rainfall forecasting and use deterministic solvers [3,26] and random programs to provide different accurate and reliable forecasts [10,14,19,20,22,25,32].…”

Section: Introductionmentioning

confidence: 99%

Rainfall trend analysis and weather forecast accuracy in selected parts of Khyber Pakhtunkhwa, Pakistan

et al. 2021

View full text Add to dashboard Cite

Climate change is a multidimensional phenomenon, which has various effects on people's environmental and socioeconomic conditions. In the agricultural economy that is susceptible to natural changes, its impact is more profound. Therefore, climate change directly affects society in different ways, and society must pay a price. Climate change, especially the changes in annual temperature and rainfall, has attracted widespread attention worldwide. The variability of these factors or the magnitude of fluctuations varies according to location. Therefore, in the context of climate change, especially in countries dominated by rainfed agriculture, studying the trend of meteorological variables is essential to assess climate-induced variations and propose feasible adaptation approaches. Focusing on this fact is the main goal of this research study was to determine the rainfall trend and the accuracy of predicted temperature at three particular stations of Khyber Pakhtunkhwa (Kp) Province, Pakistan. For this purpose, rainfall and temperature data were provided by Pakistan Meteorological Department (PMD), Islamabad, for the period 1960–2020. Two types of nonparametric techniques, Sen’s slope estimate and the Mann–Kendall test, were applied to determine a trend in the average monthly and annual rainfall. The results of the annual rainfall trend analysis showed that Peshawar and Dera Ismail Khan stations showed a positive increasing trend, while the monthly rainfall trend showed a negative decreasing trend for all stations. The trend was statistically significant for Peshawar and Saidu Sharif stations. The accuracy of predicted and actual temperature and rainfall indicated that mostly over-forecast occurred at Saidu Sharif and Peshawar. Most of the precipitation and temperature records showed under forecast for Dera Ismail Khan, but some over-prediction has also occurred. Graphical abstract

show abstract

A comparison of the rainfall forecasting skills of the WRF ensemble forecasting system using SPCPT and other cumulus parameterization error representation schemes

Cited by 9 publications

References 59 publications

Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

Mathematical Modeling for Ecosystem-Based Management (EBM) and Ecosystem Goods and Services (EGS) Assessment

Rainfall trend analysis and weather forecast accuracy in selected parts of Khyber Pakhtunkhwa, Pakistan

Contact Info

Product

Resources

About