scholarly journals Evaluation and Assimilation of FY-3C/D MWHS-2 Radiances in the RMAPS-ST

2022 ◽  
Vol 14 (2) ◽  
pp. 275
Author(s):  
Yanhui Xie ◽  
Lu Mao ◽  
Min Chen ◽  
Jiancheng Shi ◽  
Shuiyong Fan ◽  
...  
Keyword(s):  
Short Range ◽  
Positive Impact ◽  
Regional Model ◽  
Precipitation Forecast ◽  
Short Term ◽  
Low Level ◽  
Multi Scale ◽  
Quantitative Precipitation Forecast ◽  
Accumulated Rainfall ◽  
Radiance Data

Currently, humidity information can be obtained from the Microwave Humidity Sounder-2 (MWHS-2) mounted on the polar-orbiting satellites FY-3C and FY-3D. However, making full use of the MWHS-2 data remains a challenge, particularly in the application of regional numerical weather models. This study is the first to include MWHS-2 radiance data in the Rapid-refresh Multi-scale Analysis and Prediction System—Short-term (RMAPS-ST) regional model. The results and impact of MWHS-2 radiance data assimilation were investigated and evaluated. It is found that MWHS-2 radiance data can be effectively assimilated in the RMAPS-ST after a series of quality control and variational bias correction. Benefits could be obtained in the reduction of background departures for each humidity sounding channel. Assimilation experiments over a period of one month were carried out, and the impacts of MWHS-2 radiances were quantitatively analyzed on the forecasts of RMAPS-ST system. The results showed that MWHS-2 saw a small but significant improvement for low-level humidity of short-range forecast, by 16.5% and 3.2% in terms of mean bias and root-mean-square error, respectively. The positive impact on short-range forecast also can be found for middle and low level temperature and wind. For quantitative precipitation forecast, the assimilation of MWHS-2 radiances increased the score skills of different rainfall levels in the first 12 h forecast by an average of 1.4%. There was a slight overall improvement in the 24-h precipitation forecast for over-estimation and false alarm of 3-h accumulated rainfall below 1.0 mm, with 0.75% and 0.36%, respectively. The addition of MWHS-2 radiance data gives a small positive impact on low-level humidity, temperature, and wind in the RMAPS-ST regional model, and it also improves short-range forecast of rainfall, particularly in the first 12 h of the forecast.

scholarly journals Improving QPF by blending techniques at the Meteorological Service of Catalonia

2010 ◽  
Vol 10 (7) ◽  
pp. 1443-1455 ◽  
Author(s):  
A. Atencia ◽  
T. Rigo ◽  
A. Sairouni ◽  
J. Moré ◽  
J. Bech ◽  
...  
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Short Term ◽  
Convective Structures ◽  
Prediction Horizon ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Nwp Model

Abstract. The current operational very short-term and short-term quantitative precipitation forecast (QPF) at the Meteorological Service of Catalonia (SMC) is made by three different methodologies: Advection of the radar reflectivity field (ADV), Identification, tracking and forecasting of convective structures (CST) and numerical weather prediction (NWP) models using observational data assimilation (radar, satellite, etc.). These precipitation forecasts have different characteristics, lead time and spatial resolutions. The objective of this study is to combine these methods in order to obtain a single and optimized QPF at each lead time. This combination (blending) of the radar forecast (ADV and CST) and precipitation forecast from NWP model is carried out by means of different methodologies according to the prediction horizon. Firstly, in order to take advantage of the rainfall location and intensity from radar observations, a phase correction technique is applied to the NWP output to derive an additional corrected forecast (MCO). To select the best precipitation estimation in the first and second hour (t+1 h and t+2 h), the information from radar advection (ADV) and the corrected outputs from the model (MCO) are mixed by using different weights, which vary dynamically, according to indexes that quantify the quality of these predictions. This procedure has the ability to integrate the skill of rainfall location and patterns that are given by the advection of radar reflectivity field with the capacity of generating new precipitation areas from the NWP models. From the third hour (t+3 h), as radar-based forecasting has generally low skills, only the quantitative precipitation forecast from model is used. This blending of different sources of prediction is verified for different types of episodes (convective, moderately convective and stratiform) to obtain a robust methodology for implementing it in an operational and dynamic way.

scholarly journals An Assessment of Satellite Radiance Data Assimilation in RMAPS

2018 ◽  
Vol 11 (1) ◽  
pp. 54 ◽  
Author(s):  
Yanhui Xie ◽  
Shuiyong Fan ◽  
Min Chen ◽  
Jiancheng Shi ◽  
Jiqin Zhong ◽  
...  
Keyword(s):  
North China ◽  
Initial Temperature ◽  
Initial Conditions ◽  
Positive Impact ◽  
Skill Score ◽  
Limited Area ◽  
Accumulated Rainfall ◽  
Satellite Radiance Data ◽  
Radiance Data ◽  
The Impact

Due to the availability of observations and the effectiveness of bias correction, it is still a challenge to assimilate data from the polar orbit satellites into a limited-area and frequently updated model. This study assessed the initial application of satellite radiance data from multiple platforms in the Rapid-refresh Multi-scale Analysis and Prediction System (RMAPS). Satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) were used. Two 12-day retrospective runs were conducted to evaluate the impact of assimilating satellite radiance data on 0–24 h forecasts using RMAPS. The forecasts, initialized from analyses with and without satellite radiance data, were verified against observations. The results showed that satellite radiance data from AMSU-A and MHS had a positive impact on the initial conditions and the forecasts of RMAPS, even over the relatively data-rich area of North China. Compared to the control run that only assimilated conventional observations, an improvement of about 36.8% can be obtained for the temperature bias between 300 hPa and 850 hPa and 0.65% for the average RMSE. Satellite radiance observations from 1200 UTC contribute relatively significantly (77.8%) to the bias improvement of the initial temperature field. For the wind at 10 m, the bias and root-mean-square error (RMSE) both had a reduction for the 0–12 h forecast range. An improvement can be also found for the skill score of the 3-h accumulated rainfall below 10.0 mm in the first 12 h of the forecast range. There was a slight improvement in the skill score of the 6-h accumulated rainfall above 50 mm over North China, with a 20.7% improvement for the first 12 h of the forecast. The inclusion of satellite radiance observations was found to be beneficial for the initial temperature, which consequently improved the forecast skill of the 0–12 h range in the RMAPS.

scholarly journals Impact of Assimilating Ground-Based Microwave Radiometer Data on the Precipitation Bifurcation Forecast: A Case Study in Beijing

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 551
Author(s):  
Yajie Qi ◽  
Shuiyong Fan ◽  
Jiajia Mao ◽  
Bai Li ◽  
Chunwei Guo ◽  
...  
Keyword(s):  
Microwave Radiometer ◽  
Thermal Conditions ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Multi Scale ◽  
Quantitative Precipitation Forecast ◽  
Radar Echo ◽  
Multiple Cycle ◽  
Microwave Radiometers ◽  
The Impact

In this study, the temperature and relative humidity profiles retrieved from five ground-based microwave radiometers in Beijing were assimilated into the rapid-refresh multi-scale analysis and prediction system-short term (RMAPS-ST). The precipitation bifurcation prediction that occurred in Beijing on 4 May 2019 was selected as a case to evaluate the impact of their assimilation. For this purpose, two experiments were set. The Control experiment only assimilated conventional observations and radar data, while the microwave radiometers profilers (MWRPS) experiment assimilated conventional observations, the ground-based microwave radiometer profiles and radar data into the RMAPS-ST model. The results show that in comparison with the Control test, the MWRPS test made reasonable adjustments for the thermal conditions in time, better reproducing the weak heat island phenomenon in the observation prior to the rainfall. Thus, assimilating MWRPS improved the skills of the precipitation forecast in both the distribution and the intensity of rainfall precipitation, capable of predicting the process of belt-shaped radar echo splitting and the precipitation bifurcation in the urban area of Beijing. The assimilation of the ground-based microwave radiometer profiles improved the skills of the quantitative precipitation forecast to a certain extent. Among multiple cycle experiments, the onset of 0600 UTC cycle closest to the beginning of rainfall performed best by assimilating the ground-based microwave radiometer profiles.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

scholarly journals Paleolithic Diet—Effect on the Health Status and Performance of Athletes?

Nutrients ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 1019
Author(s):  
Barbara Frączek ◽  
Aleksandra Pięta ◽  
Adrian Burda ◽  
Paulina Mazur-Kurach ◽  
Florentyna Tyrała
Keyword(s):  
Blood Pressure ◽  
Body Mass ◽  
Positive Impact ◽  
Model Assessment ◽  
Paleolithic Diet ◽  
Short Term ◽  
Positive Effects ◽  
Short And Long Term ◽  
The Impact

The aim of this meta-analysis was to review the impact of a Paleolithic diet (PD) on selected health indicators (body composition, lipid profile, blood pressure, and carbohydrate metabolism) in the short and long term of nutrition intervention in healthy and unhealthy adults. A systematic review of randomized controlled trials of 21 full-text original human studies was conducted. Both the PD and a variety of healthy diets (control diets (CDs)) caused reduction in anthropometric parameters, both in the short and long term. For many indicators, such as weight (body mass (BM)), body mass index (BMI), and waist circumference (WC), impact was stronger and especially found in the short term. All diets caused a decrease in total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG), albeit the impact of PD was stronger. Among long-term studies, only PD cased a decline in TC and LDL-C. Impact on blood pressure was observed mainly in the short term. PD caused a decrease in fasting plasma (fP) glucose, fP insulin, and homeostasis model assessment of insulin resistance (HOMA-IR) and glycated hemoglobin (HbA1c) in the short run, contrary to CD. In the long term, only PD caused a decrease in fP glucose and fP insulin. Lower positive impact of PD on performance was observed in the group without exercise. Positive effects of the PD on health and the lack of experiments among professional athletes require longer-term interventions to determine the effect of the Paleo diet on athletic performance.

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