scholarly journals Estimation of relative humidity profiles From INSAT cloud data

MAUSAM ◽  
2021 ◽  
Vol 42 (3) ◽  
pp. 287-294
Author(s):  
ONKARI PRASAD ◽  
A.V. R. K. RAO
Keyword(s):  
Relative Humidity ◽  
Prediction Models ◽  
Weather Prediction ◽  
Cloud Amount ◽  
Moisture Distribution ◽  
Total Cloud Amount ◽  
Cloud Data ◽  
The Indian Ocean ◽  
Moisture Profiles ◽  
Humidity Profiles

Accurate humidity profiles are needed for obtaining useful rainfall forecasts from numerical weather prediction models. In this context objective estimation of moisture profiles over ocean areas using satellite cloud data becomes important. For this purpose the fractional cloudiness data available from INSAT has been classified into different cloud categories depending on the total cloud amount and the levels at which the clouds have been present. Actual relative humidity profiles have been obtained using TEMP data of Port Blair (11 .6°N 92.7°E) and Minicoy (8,3°N, 72,9°E), Most frequently occurring relative humidity profile has been selected as being representative of humidity distribution in the vertical for a given cloud category. The preliminary results reported here show that these bogus relative humidity profiles could provide useful Information on moisture distribution in the vertical over the Indian Ocean.  

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

scholarly journals A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts

2021 ◽  
Author(s):  
Lei Han ◽  
Mingxuan Chen ◽  
Kangkai Chen ◽  
Haonan Chen ◽  
Yanbiao Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Relative Humidity ◽  
Wind Direction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Ground Truth ◽  
Learning Method ◽  
Weather Variables ◽  
Medium Range Weather Forecast ◽  
Forecast Lead Time

AbstractCorrecting the forecast bias of numerical weather prediction models is important for severe weather warnings. The refined grid forecast requires direct correction on gridded forecast products, as opposed to correcting forecast data only at individual weather stations. In this study, a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model (ECMWF-IFS): 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction, with a forecast lead time of 24 h to 240 h in North China. First, the forecast correction problem is transformed into an image-to-image translation problem in deep learning under the CU-net architecture, which is based on convolutional neural networks. Second, the ECMWF-IFS forecasts and ECMWF reanalysis data (ERA5) from 2005 to 2018 are used as training, validation, and testing datasets. The predictors and labels (ground truth) of the model are created using the ECMWF-IFS and ERA5, respectively. Finally, the correction performance of CU-net is compared with a conventional method, anomaly numerical correction with observations (ANO). Results show that forecasts from CU-net have lower root mean square error, bias, mean absolute error, and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h. CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics, whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity. For the correction of the 10-m wind direction forecast, which is often difficult to achieve, CU-net also improves the correction performance.

scholarly journals Recent trends in meteorological parameters over Jammu and Kashmir

MAUSAM ◽  
2021 ◽  
Vol 61 (3) ◽  
pp. 369-382
Author(s):  
A. K. JASWAL ◽  
G. S. PRAKASA RAO
Keyword(s):  
Relative Humidity ◽  
Minimum Temperature ◽  
Meteorological Parameters ◽  
Cloud Amount ◽  
The State ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Jammu And Kashmir ◽  
Total Cloud Amount ◽  
Increasing Trends

Annual trends of meteorological parameters temperature, rainfall, relative humidity and clouds for ten stations in Jammu and Kashmir during the period 1976-2007 were studied. Trend analysis shows that temperatures are increasing over the state with significant increase in maximum temperature in the Kashmir region (+0.04 to                +  0.05° C/year) and minimum temperature in the Jammu region (+0.03 to + 0.08° C/year). The diurnal temperature range (DTR) is increasing over Kashmir region due to higher increasing trends in the maximum temperature while the strong increasing trends in the minimum temperature are contributing more towards the decrease in DTR over the Jammu region. Annual rainfall and rainy days trends are decreasing in both the regions of the state except at Jammu where rainfall trend is significantly increasing (+12.05 mm/year). Day-time relative humidity trends are mixed while total cloud amount trends are decreasing over Kashmir region and increasing over Jammu region. The effects of urbanization in the last two decades are more pronounced in Jammu region and this is strongly expressed in minimum temperature over the region. The warming trends observed over Jammu and Kashmir state during the period of study need further investigation in relation to variability of atmospheric circulation over North India.

scholarly journals Shallow Cumulus in WRF Parameterizations Evaluated against LASSO Large-Eddy Simulations

2018 ◽  
Vol 146 (12) ◽  
pp. 4303-4322 ◽  
Author(s):  
Wayne M. Angevine ◽  
Joseph Olson ◽  
Jaymes Kenyon ◽  
William I. Gustafson ◽  
Satoshi Endo ◽  
...  
Keyword(s):  
Mass Flux ◽  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Cloud Amount ◽  
Large Eddy Simulations ◽  
Shallow Cumulus ◽  
Wide Range ◽  
Large Eddy ◽  
Single Column

AbstractRepresentation of shallow cumulus is a challenge for mesoscale numerical weather prediction models. These cloud fields have important effects on temperature, solar irradiance, convective initiation, and pollutant transport, among other processes. Recent improvements to physics schemes available in the Weather Research and Forecasting (WRF) Model aim to improve representation of shallow cumulus, in particular over land. The DOE LES ARM Symbiotic Simulation and Observation Workflow (LASSO) project provides several cases that we use here to test the new physics improvements. The LASSO cases use multiple large-scale forcings to drive large-eddy simulations (LES), and the LES output is easily compared to output from WRF single-column simulations driven with the same initial conditions and forcings. The new Mellor–Yamada–Nakanishi–Niino (MYNN) eddy diffusivity mass-flux (EDMF) boundary layer and shallow cloud scheme produces clouds with timing, liquid water path (LWP), and cloud fraction that agree well with LES over a wide range of those variables. Here we examine those variables and test the scheme’s sensitivity to perturbations of a few key parameters. We also discuss the challenges and uncertainties of single-column tests. The older, simpler total energy mass-flux (TEMF) scheme is included for comparison, and its tuning is improved. This is the first published use of the LASSO cases for parameterization development, and the first published study to use such a large number of cases with varying cloud amount. This is also the first study to use a more precise combined infrared and microwave retrieval of LWP to evaluate modeled clouds.

scholarly journals A Strategy for Verifying Near-Convection-Resolving Model Forecasts at Observing Sites

2014 ◽  
Vol 29 (2) ◽  
pp. 185-204 ◽  
Author(s):  
Marion P. Mittermaier
Keyword(s):  
Positive Impact ◽  
Weather Prediction ◽  
Grid Point ◽  
Probabilistic Approach ◽  
Cloud Amount ◽  
Ensemble Prediction ◽  
Cloud Base ◽  
Brier Score ◽  
Total Cloud Amount ◽  
Grid Points

Abstract Routine verification of deterministic numerical weather prediction (NWP) forecasts from the convection-permitting 4-km (UK4) and near-convection-resolving 1.5-km (UKV) configurations of the Met Office Unified Model (MetUM) has shown that it is hard to consistently demonstrate an improvement in skill from the higher-resolution model, even though subjective comparison suggests that it performs better. In this paper the use of conventional metrics and precise matching (through extracting the nearest grid point to an observing site) of the forecast to conventional synoptic observations in space and time is replaced with the use of inherently probabilistic metrics such as the Brier score, ranked probability, and continuous ranked probability scores applied to neighborhoods of forecast grid points. Three neighborhood sizes were used: ~4, ~12, and ~25 km, which match the sizes of the grid elements currently used operationally. Six surface variables were considered: 2-m temperature, 10-m wind speed, total cloud amount (TCA), cloud-base height (CBH), visibility, and hourly precipitation. Any neighborhood has a positive impact on skill, either in reducing the skill deficit or enhancing the skillfulness over and above the single grid point. This is true for all variables. An optimal neighborhood appears to depend on the variable and threshold. Adopting this probabilistic approach enables easy comparison to future near-convection-resolving ensemble prediction systems (EPS) and also enables the optimization of postprocessing to maximize the skill of forecast products.

scholarly journals Ensemble versus Deterministic Performance at the Kilometer Scale

2017 ◽  
Vol 32 (5) ◽  
pp. 1697-1709 ◽  
Author(s):  
M. P. Mittermaier ◽  
G. Csima
Keyword(s):  
Lead Time ◽  
Deterministic Model ◽  
Weather Prediction ◽  
Cloud Amount ◽  
Ensemble Prediction ◽  
Cloud Base ◽  
Spatial Accuracy ◽  
Ensemble Prediction System ◽  
Total Cloud Amount ◽  
Variable Resolution

Abstract What is the benefit of a near-convection-resolving ensemble over a near-convection-resolving deterministic forecast? In this paper, a way in which ensemble and deterministic numerical weather prediction (NWP) systems can be compared is demonstrated using a probabilistic verification framework. Three years’ worth of raw forecasts from the Met Office Unified Model (UM) 12-member 2.2-km Met Office Global and Regional Ensemble Prediction System (MOGREPS-UK) ensemble and 1.5-km Met Office U.K. variable resolution (UKV) deterministic configuration were compared, utilizing a range of forecast neighborhood sizes centered on surface synoptic observing site locations. Six surface variables were evaluated: temperature, 10-m wind speed, visibility, cloud-base height, total cloud amount, and hourly precipitation. Deterministic forecasts benefit more from the application of neighborhoods, though ensemble forecast skill can also be improved. This confirms that while neighborhoods can enhance skill by sampling more of the forecast, a single deterministic model state in time cannot provide the variability, especially at the kilometer scale, where rapid error growth acts to limit local predictability. Ensembles are able to account for the uncertainty at larger, synoptic scales. The results also show that the rate of decrease in skill with lead time is greater for the deterministic UKV. MOGREPS-UK retains higher skill for longer. The concept of a skill differential is introduced to find the smallest neighborhood size at which the deterministic and ensemble scores are comparable. This was found to be 3 × 3 (6.6 km) for MOGREPS-UK and 11 × 11 (16.5 km) for UKV. Comparable scores are between 2% and 40% higher for MOGREPS-UK, depending on the variable. Naively, this would also suggest that an extra 10 km in spatial accuracy is gained by using a kilometer-scale ensemble.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

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