scholarly journals A REFINING METHOD OF NON-LINEAR REGIONAL TM MODEL BASED ON RANDOM FOREST

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1169-1174
Author(s):  
Q. T. Wan ◽  
L. L. Liu ◽  
L. K. Huang ◽  
W. Zhou ◽  
Y. Z. Yang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Random Forest ◽  
Surface Temperature ◽  
High Precision ◽  
Time Scale ◽  
Coastal Areas ◽  
Eastern Coast ◽  
Observation Data ◽  
Improvement Effect ◽  
Tm Model

Abstract. Weighted mean temperature (Tm) is a critical parameters in GNSS technology to retrieve precipitable water vaper (PWV). By obtaining high-precision Tm, it can provide an important reference data source for regional strong convective weather and large-scale climate anomalies. The high-precision Tm of most areas can be obtained by using the BEVIS model and the surface temperature (Ts). The eastern coastal areas of China are affected by the monsoon climate, which makes the applicability of the method in this area to be improved. The research shows that the Tm which calculated by Fourier series analysis (FTm model) has better applicability in the region than the BEVIS model. However, the method has a single modeling factor, and the precision improvement effect in some area is not obvious. By using the observation data of 13 radiosonde stations in the eastern coastal areas of China from 2010 to 2015. Tm which calculated by numerical integration is used as the reference of the true value. Four of the observation data are selected by the method of random forest (RF). The eigenvalues include the pressure、surface temperature、water vapor pressure and specific humidity are used as input factors. The prediction corrections are added to the deviation of FTm model, and a new Tm is applied to the eastern coast of China which called RFF Tm. Taking the observation data from 2010 to 2014 as the training database, the research area is divided into three areas from south to north according to the latitude. The prediction results of different time scales are studied by the clamping criterion, and then the prediction of random forest is discussed. The correction effect is adaptable in the eastern coast areas of China. The results show that: (1) The RFF Tm model refinement method based on random forest has better adaptability in eastern coastal areas of China, and the applicability of first area is more stable with the prediction time scale than the FTm model. (2) On the time scale with a forecast period of one year, MAE and RMS are 4.7 and 4.6 in third area, 3.2 and 3.8 in second area, and 2.6 and 2.5 in first area. (3) The improvement effect of random forests in the eastern coastal areas of China gradually increases with the prediction period becoming shorter. The predicted deviation values of the eastern coast areas of China reach a steady state when the period is one month. The correction deviations is within 1.5K. The correction range of the third area is better than the second area and first area, which makes up for the shortcomings of the FTm model with low precision in the region. It can be used as a new multi-factor prediction and correction Tm model for GNSS remote sensing water vapor in the eastern coastal areas of China.

scholarly journals Combining Meteosat-10 satellite image data with GPS tropospheric path delays to estimate regional integrated water vapor (IWV) distribution

2017 ◽  
Vol 10 (2) ◽  
pp. 537-548 ◽  
Author(s):  
Anton Leontiev ◽  
Yuval Reuveni
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Weather Prediction ◽  
Satellite Image ◽  
Image Data ◽  
Lower Atmosphere ◽  
Observation Data ◽  
Pixel Intensity ◽  
Gps Satellites ◽  
Prediction Systems

Abstract. Using GPS satellites signals, we can study different processes and coupling mechanisms that can help us understand the physical conditions in the lower atmosphere, which might lead or act as proxies for severe weather events such as extreme storms and flooding. GPS signals received by ground stations are multi-purpose and can also provide estimates of tropospheric zenith delays, which can be converted into accurate integrated water vapor (IWV) observations using collocated pressure and temperature measurements on the ground. Here, we present for the first time the use of Israel's dense regional GPS network for extracting tropospheric zenith path delays combined with near-real-time Meteosat-10 water vapor (WV) and surface temperature pixel intensity values (7.3 and 10.8 µm channels, respectively) in order to assess whether it is possible to obtain absolute IWV (kg m−2) distribution. The results show good agreement between the absolute values obtained from our triangulation strategy based solely on GPS zenith total delays (ZTD) and Meteosat-10 surface temperature data compared with available radiosonde IWV absolute values. The presented strategy can provide high temporal and special IWV resolution, which is needed as part of the accurate and comprehensive observation data integrated in modern data assimilation systems and is required for increasing the accuracy of regional numerical weather prediction systems forecast.

scholarly journals CMIP5-Based Projection of Decadal and Seasonal Sea Surface Temperature Variations in East China Shelf Seas

2021 ◽  
Vol 9 (4) ◽  
pp. 367
Author(s):  
Huiqiang Lu ◽  
Chuan Xie ◽  
Cuicui Zhang ◽  
Jingsheng Zhai
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
The Yellow Sea ◽  
East China ◽  
Cmip5 Models ◽  
Projection Data ◽  
Observation Data ◽  
Model Intercomparison ◽  
Shelf Seas

The East China Shelf Seas, comprising the Bohai Sea, the Yellow Sea, and the shelf region of East China Sea, play significant roles among the shelf seas of the Western North Pacific Ocean. The projection of sea surface temperature (SST) changes in these regions is a hot research topic in marine science. However, this is a very difficult task due to the lack of available long-term projection data. Recently, with the high development of simulation technology based on numerical models, the model intercomparison projects, e.g., Phase 5 of the Climate Model Intercomparison Project (CMIP5), have become important ways of understanding climate changes. CMIP5 provides multiple models that can be used to estimate SST changes by 2100 under different representative concentration pathways (RCPs). This paper developed a CMIP5-based SST investigation framework for the projection of decadal and seasonal variation of SST in East China Shelf Seas by 2100. Since the simulation results of CMIP5 models may have degrees of errors, this paper uses hydrological observation data from World Ocean Atlas 2018 (WOA18) for model validation and correction. This paper selects seven representative ones including ACCESS1.3, CCSM4, FIO-ESM, CESM1-CAM5, CMCC-CMS, NorESM1-ME, and Max Planck Institute Earth System Model of medium resolution (MPI-ESM-MR). The decadal and seasonal SST changes in the next 100 years (2030, 2060, 2090) are investigated by comparing with the present analysis in 2010. The experimental results demonstrate that SST will increase significantly by 2100: the decadal SST will increase by about 1.55 °C, while the seasonal SST will increase by 1.03–1.95 °C.

scholarly journals Visibility Prediction over South Korea Based on Random Forest

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 552
Author(s):  
Bu-Yo Kim ◽  
Joo Wan Cha ◽  
Ki-Ho Chang ◽  
Chulkyu Lee
Keyword(s):  
Random Forest ◽  
South Korea ◽  
Correlation Coefficients ◽  
Predictive Performance ◽  
Air Pollutant ◽  
Economic Losses ◽  
Observation Data ◽  
Local Data ◽  
Weather Forecasts ◽  
Monitoring Service

In this study, the visibility of South Korea was predicted (VISRF) using a random forest (RF) model based on ground observation data from the Automated Synoptic Observing System (ASOS) and air pollutant data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) model. Visibility was predicted and evaluated using a training set for the period 2017–2018 and a test set for 2019. VISRF results were compared and analyzed using visibility data from the ASOS (VISASOS) and the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (VISLDAPS) operated by the Korea Meteorological Administration (KMA). Bias, root mean square error (RMSE), and correlation coefficients (R) for the VISASOS and VISLDAPS datasets were 3.67 km, 6.12 km, and 0.36, respectively, compared to 0.14 km, 2.84 km, and 0.81, respectively, for the VISASOS and VISRF datasets. Based on these comparisons, the applied RF model offers significantly better predictive performance and more accurate visibility data (VISRF) than the currently available VISLDAPS outputs. This modeling approach can be implemented by authorities to accurately estimate visibility and thereby reduce accidents, risks to public health, and economic losses, as well as inform on urban development policies and environmental regulations.

scholarly journals A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution

2021 ◽  
Vol 13 (11) ◽  
pp. 2211
Author(s):  
Shuo Xu ◽  
Jie Cheng ◽  
Quan Zhang
Keyword(s):  
Random Forest ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Learning Algorithm ◽  
Mainland China ◽  
Fusion Method ◽  
Moisture Condition

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.

Earth's balanced climates in view of their energy budgets

2021 ◽  
Author(s):  
Thomas Anderl
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Energy Budget ◽  
Energy Budgets ◽  
Temperature Dependency ◽  
Energy Balancing ◽  
Atmospheric Water ◽  
Atmospheric Absorption ◽  
Rainfall Events ◽  
Characteristic Concentration

Abstract Earth’s well-known energy budget scheme is subjected to variations representing changes of insolation and atmospheric absorption. The Charney Report variability cases of doubled atmospheric CO2 concentration and insolation increase by 2 % are found reproducible. The planetary emissivity is revealed linear to surface temperature, conformant with measurements. Atmospheric water vapor with its characteristic concentration-temperature dependency appears as a major component in Earth’s energy balancing mechanisms. From this, shift towards fewer and stronger rainfall events is prescribed for rising temperatures.

Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation

1994 ◽  
Vol 99 (C3) ◽  
pp. 5219 ◽  
Author(s):  
William J. Emery ◽  
Yunyue Yu ◽  
Gary A. Wick ◽  
Peter Schluessel ◽  
Richard W. Reynolds
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water

scholarly journals High-Precision Bidecadal Calibration of the Radiocarbon Time Scale, 500–2500 BC

Radiocarbon ◽  
1993 ◽  
Vol 35 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Gordon W. Pearson ◽  
Minze Stuiver
Keyword(s):  
High Precision ◽  
Time Scale ◽  
Count Rate ◽  
Basic Information ◽  
Data Set ◽  
Sole Purpose ◽  
Radon Decay

The sole purpose of this paper is to present a previously published 14C data set to which minor corrections have been applied. All basic information previously given is still applicable (Pearson & Stuiver 1986). The corrections are needed because 14C count-rate influences (radon decay in Seattle, a re-evaluation of the corrections applied for efficiency variation with time previously unrecognized in Belfast) had to be accounted for in more detail. Information on the radon correction is given in Stuiver and Becker (1993). The Belfast corrections were necessary because the original correction for efficiency variations with time was calculated using two suspect standards (these were shown to be suspect by recent observations) that overweighted the correction. A re-evaluation (Pearson & Qua 1993) now shows it to be almost insignificant, and the corrected dates (using the new correction) became older by about 16 years.

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