scholarly journals On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations and MERRA and ECMWF data sets

2015 ◽  
Vol 8 (4) ◽  
pp. 1789-1797 ◽  
Author(s):  
P. Vergados ◽  
A. J. Mannucci ◽  
C. O. Ao ◽  
J. H. Jiang ◽  
H. Su
Keyword(s):  
Relative Humidity ◽  
General Circulation ◽  
Intraseasonal Variability ◽  
Seasonal Fluctuation ◽  
Weather Forecast ◽  
Hadley Cell ◽  
Data Sets ◽  
Cosmic Radio ◽  
Medium Range Weather Forecast ◽  
Middle Troposphere

Abstract. The spatial variability of the tropical tropospheric relative humidity (RH) throughout the vertical extent of the troposphere is examined using Global Positioning System Radio Occultation (GPSRO) observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. These high vertical resolution observations capture the detailed structure and moisture budget of the Hadley Cell circulation. We compare the COSMIC observations with the European Center for Medium-range Weather Forecast (ECMWF) Reanalysis Interim (ERA-Interim) and the Modern-Era Retrospective analysis for Research and Applications (MERRA) climatologies. Qualitatively, the spatial pattern of RH in all data sets matches up remarkably well, capturing distinct features of the general circulation. However, RH discrepancies exist between ERA-Interim and COSMIC data sets that are noticeable across the tropical boundary layer. Specifically, ERA-Interim shows a drier Intertropical Convergence Zone (ITCZ) by 15–20% compared to both COSMIC and MERRA data sets, but this difference decreases with altitude. Unlike ECMWF, MERRA shows an excellent agreement with the COSMIC observations except above 400 hPa, where GPSRO observations capture drier air by 5–10%. RH climatologies were also used to evaluate intraseasonal variability. The results indicate that the tropical middle troposphere at ±5–25° is most sensitive to seasonal variations. COSMIC and MERRA data sets capture the same magnitude of the seasonal variability, but ERA-Interim shows a weaker seasonal fluctuation up to 10% in the middle troposphere inside the dry air subsidence regions of the Hadley Cell. Over the ITCZ, RH varies by maximum 9% between winter and summer.

scholarly journals On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations, MERRA and ECMWF data sets

2015 ◽  
Vol 8 (1) ◽  
pp. 517-540
Author(s):  
P. Vergados ◽  
A. J. Mannucci ◽  
C. O. Ao ◽  
J. H. Jiang ◽  
H. Su
Keyword(s):  
Relative Humidity ◽  
General Circulation ◽  
Intraseasonal Variability ◽  
Seasonal Fluctuation ◽  
Hadley Cell ◽  
Data Sets ◽  
Cosmic Radio ◽  
Inter Tropical Convergence Zone ◽  
Medium Range Weather Forecast ◽  
Middle Troposphere

Abstract. The spatial variability of the tropical tropospheric relative humidity (RH) throughout the vertical extent of the troposphere is examined using Global Positioning System Radio Occultation (GPSRO) observations from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) mission. These high vertical resolution observations capture the detailed structure and moisture budget of the Hadley Cell circulation. We compare the COSMIC observations with the European Center for Medium-range Weather Forecast (ECMWF) Re-Analysis Interim (ERA-Interim) and the Modern-Era Retrospective analysis for Research and Applications (MERRA) climatologies. Qualitatively, the spatial pattern of RH in all data sets matches up remarkably well, capturing distinct features of the general circulation. However, RH discrepancies exist between ERA-Interim and COSMIC data sets, which are noticeable across the tropical boundary layer. Specifically, ERA-Interim shows a drier Inter Tropical Convergence Zone (ITCZ) by 15–20% compared both to COSMIC and MERRA data sets, but this difference decreases with altitude. Unlike ECMWF, MERRA shows an excellent agreement with the COSMIC observations except above 400 hPa, where GPSRO observations capture drier air by 5–10%. RH climatologies were also used to evaluate intraseasonal variability. The results indicate that the tropical middle troposphere at ±5–25° is most sensitive to seasonal variations. COSMIC and MERRA data sets capture the same magnitude of the seasonal variability, but ERA-Interim shows a weaker seasonal fluctuation up to 10% in the middle troposphere inside the dry air subsidence regions of the Hadley Cell. Over the ITCZ, RH varies by maximum 9% between winter and summer.

scholarly journals Numerical Weather Prediction for Himalayan Complex Terrain: Prospects of Variational Data Assimilation

2016 ◽  
Vol 3 (1) ◽  
pp. 67
Author(s):  
Sangeeta Maharjan ◽  
Ram P. Regmi
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Environmental Research ◽  
Data Sets ◽  
Observation Data ◽  
Ongoing Research ◽  
Medium Range Weather Forecast ◽  
Research Activities ◽  
Wrf Modeling

<p>As part of the ongoing research activities at National Atmospheric Resource and Environmental Research Laboratory (NARERL) to realize high spatial and temporal resolution weather forecasts for Nepal, the Weather Research and Forecasting (WRF) modeling system performance with the National Center for Environmental Protection (NCEP) and National Center for Medium Range Weather Forecast (NCMRWF) initialization global meteorological data sets and the effect of surface observation data assimilation have been examined. The study shows that WRF modeling system reasonably well predicts the diurnal variation of upcoming weather events with both the data sets. The observation data assimilation from entire weather station distributed over the country may lead to the significant improvement in the accuracy and reliability of extended period of forecast. However, upper air observation data assimilation would be necessary to achieve desired precision and reliability of extended weather forecast.</p><p>Journal of Nepal Physical Society Vol.3(1) 2015: 67-72</p>

scholarly journals Statistical interpretation of general circulation model: A prospect for automation of medium range local weather forecast in India

MAUSAM ◽  
2021 ◽  
Vol 47 (3) ◽  
pp. 229-236
Author(s):  
ASHOK KUMAR ◽  
PARVINDER MAINI
Keyword(s):  
General Circulation ◽  
Weather Forecasting ◽  
Monsoon Season ◽  
Circulation Model ◽  
Weather Forecast ◽  
General Circulation Models ◽  
Statistical Interpretation ◽  
Medium Range Weather Forecast ◽  
Medium Range ◽  
Local Weather

The General Circulation Models (GCM), though able to provide reasonably good medium range weather forecast. have comparatively less skill in forecasting location-specific weather. This is mainly due to the poor representation of 16cal topography and other features in these models. Statistical interpretation (SI) of GCM is very essential in order to improve the location-specific medium range local weather forecast. An attempt has been made at the National Centre for Medium Range Weather Forecasting (NCMRWF), New Delhi to do this type of objective forecasting. Hence location-specific SI models are developed and a bias free forecast is obtained. One of the techniques for accomplishing this, is the Perfect Prog. Method (PPM). PPM models for precipitation (quantitative, probability, yes/no) and maximum minimum temperature are developed for monsoon season (June to August) for 10 stations in lndia. These PPM models and the output from the GCM (R-40) operational at NCMRWF, are then used to obtain the SI forecast. An indirect method based upon SI forecast and observed values of previous one or two seasons, for getting bias free forecast is explained. A comparative study of skill of bias free SI and final forecast, with the observed, issued from NCMRWF to 10 Agromet Field Units (AMFU) during monsoon season 1993, has indicated that automation of medium range local weather forecast can be achieved with the help of SI forecast.

scholarly journals Validation of COSMIC water vapor data in the upper troposphere and lower stratosphere using MLS, MERRA and ERA-Interim

2016 ◽  
Author(s):  
Ming Shangguan ◽  
Katja Matthes ◽  
Wuke Wang ◽  
Tae-Kwon Wee
Keyword(s):  
Water Vapor ◽  
Seasonal Cycle ◽  
Lower Stratosphere ◽  
Southern Oscillation ◽  
Weather Forecast ◽  
Cosmic Radio ◽  
Quasi Biennial Oscillation ◽  
Medium Range Weather Forecast ◽  
Upper Troposphere ◽  
The Antarctic

Abstract. Water vapor is the most important greenhouse gas in the atmosphere with important implications not only for the Earth’s radiation and energy budget but also for various chemical, physical and dynamical processes in the stratosphere. The Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Radio Occultation (RO) dataset from 2007 through 2013 is used for the first time to study the distribution and variability water vapor in the upper troposphere and lower stratosphere (UTLS). The COSMIC data are compared to the Microwave Limb Sounder (MLS) data, and to two global reanalyses: The Modern-Era Retrospective analysis for Research and Application (MERRA) of the National Aeronautics and Space Administration (NASA); and, the latest reanalysis of the European Center for Medium-range Weather Forecast (ECMWF), the ERA-Interim. The MLS data have been assimilated into the MERRA, whereas the COSMIC data are used for the ERA-Interim. As a result, the MERRA agrees well with the MLS data and so does the ERA-Interim with the COSMIC data. While the monthly zonal mean distributions of water vapor from the four datasets show good agreements in northern mid-latitudes, large discrepancies exist in high southern latitudes and tropics. The MERRA shows overall a consistent seasonal cycle with MLS, but has too strong winter dehydration over the Antarctic, and is very weak in the interannual variations. The ERA-Interim fails to properly represent the winter dehydration over the Antarctic, and shows an unrealistic seasonal cycle in the tropical upper troposphere. The COSMIC data shows a good agreement with the MLS data except for the tropical "taper recorder" signal, where the COSMIC data suggest a faster upward motion than the MLS data. The COSMIC data are able to represent the moisture variabilities associated with the Quasi-Biennial Oscillation and the El Niño-Southern Oscillation.

ECMWF versus Hellermann & Rosenstein stress climatology of the Southern Ocean

1992 ◽  
Vol 4 (1) ◽  
pp. 111-117 ◽  
Author(s):  
Rita Glowienka-Hense ◽  
Andreas Hense ◽  
Christoph Völker
Keyword(s):  
Weather Forecast ◽  
Weddell Sea ◽  
Small Scale ◽  
Data Sets ◽  
Independent Data ◽  
High Latitudes ◽  
Medium Range Weather Forecast ◽  
Meridional Component ◽  
Medium Range ◽  
Observational Errors

A time series of wind stresses computed from European Centre for Medium Range Weather Forecast (ECMWF) wind data is compared to the climatology of Hellermann & Rosenstein (HR) for the Southern Hemisphere. ECMWF stresses are generally stronger, especially in the westerly belt. However they have an overall lower meridional component than the HR data. The dominance of the half annual cycle relative to the annual wave in the zonal stress at middle to high latitudes, which is documented for independent data sets, is seen in the ECMWF but not in the HR data. ECMWF winds are also compared with measurements from three expeditions to the Weddell Sea by RV Polarstern. Good correlations between Polarstern and ECMWF winds are found but for single dates the differences are above 10 ms −1. The differences are found to be uncorrelated in space and are thus due to observational errors and to the unresolved small scale variance in the ECMWF analysed winds.

scholarly journals A 1D Var Retrieval of Relative Humidity Using the ERA5 Dataset for the Assimilation of Raman Lidar Measurements

2020 ◽  
Vol 37 (11) ◽  
pp. 2051-2064
Author(s):  
S. Mahagammulla Gamage ◽  
R. J. Sica ◽  
G. Martucci ◽  
A. Haefele
Keyword(s):  
Relative Humidity ◽  
Saturation Pressure ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Weather Forecast ◽  
Data Retrieval ◽  
Uncertainty Budget ◽  
Raman Lidar ◽  
Medium Range Weather Forecast ◽  
Humidity Profiles

AbstractWe present a one-dimensional variational (1D Var) retrieval of fifth-generation European Centre for Medium-Range Weather Forecast reanalysis (ERA5) temperature and relative humidity profiles above Payerne, Switzerland, assimilating raw backscatter measurements from the MeteoSwiss Raman Lidar for Meteorological Observations (RALMO). Our reanalysis is called ERA5-reRH. We use an optimal estimation method to perform the 1D Var data retrieval. The forward model combines the Raman lidar equation with the Hyland and Wexler expression for water vapor saturation pressure. The error covariance matrix of ERA5 was derived from the differences between ERA5 and a set of 50 special radiosoundings that have not been assimilated into ERA5. We validate ERA5-reRH, ERA5, and RALMO temperature and relative humidity profiles against the same set of special radiosoundings and found the best agreement was with our reanalysis, with a bias of less than 2% relative humidity with respect to water (%RHw) and a spread of less than 8%RHw below 8 km in terms of relative humidity. Improvements for temperature in our reanalysis are only found in the boundary layer, as ERA5 assimilates a large number of upper-air temperature observations. Our retrieval also provides a full uncertainty budget of the reanalyzed temperature and relative humidity including both random and systematic uncertainties.

scholarly journals Integration of a relocatable ocean model in the Mediterranean Forecasting System

2006 ◽  
Vol 3 (5) ◽  
pp. 1609-1621 ◽  
Author(s):  
A. Russo ◽  
A. Coluccelli
Keyword(s):  
General Circulation ◽  
Forecast Accuracy ◽  
Weather Forecast ◽  
General Circulation Models ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Medium Range Weather Forecast ◽  
Forecasting System ◽  
The Mediterranean ◽  
Environmental Prediction

Abstract. The MFS (Mediterranean Forecasting System) project and its follower MFSTEP (Mediterranean ocean Forecasting System–Towards Environmental Prediction) are being covering the Mediterranean Sea with operational Ocean General Circulation Models (OGCMs) at horizontal resolution varying from about 12 km till 2005 to 6.5 km in 2006 (reaching 3 km with some regional models and 1.5 km for few shelf models). Heat, water and momentum fluxes through the air-sea interface are derived from the European Center for Medium-range Weather Forecast (ECMWF) output at 0.5° horizontal resolution. Such horizontal resolutions could be not able to provide the needed forecast accuracy in some cases (localized emergencies at sea, e.g. oil spill; need for high resolution current forecasts, e.g. offshore works). A solution to this problem is represented by relocatable models able to be rapidly deployed and to produce forecasts starting from the MFS products. The Harvard Ocean Prediction System (HOPS) has been chosen as base of the relocatable model and it has been interfaced with the MFSTEP OGCM and one regional model. The relocatable model has demonstrated capability to produce forecasts within 2-3 days in many cases, and more rapid implementation may be obtained.

scholarly journals A Comparison between One-Step and Two-Step Nesting Strategy in the Dynamical Downscaling of Regional Climate Model COSMO-CLM at 2.2 km Driven by ERA5 Reanalysis

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 260
Author(s):  
Mario Raffa ◽  
Alfredo Reder ◽  
Marianna Adinolfi ◽  
Paola Mercogliano
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Weather Forecast ◽  
Medium Range Weather Forecast ◽  
Spatial And Temporal Scales ◽  
Starting Point ◽  
One Step

Recently, the European Centre for Medium Range Weather Forecast (ECMWF) has released a new generation of reanalysis, acknowledged as ERA5, representing at the present the most plausible picture for the current climate. Although ERA5 enhancements, in some cases, its coarse spatial resolution (~31 km) could still discourage a direct use of precipitation fields. Such a gap could be faced dynamically downscaling ERA5 at convection permitting scale (resolution < 4 km). On this regard, the selection of the most appropriate nesting strategy (direct one-step against nested two-step) represents a pivotal issue for saving time and computational resources. Two questions may be raised within this context: (i) may the dynamical downscaling of ERA5 accurately represents past precipitation patterns? and (ii) at what extent may the direct nesting strategy performances be adequately for this scope? This work addresses these questions evaluating two ERA5-driven experiments at ~2.2 km grid spacing over part of the central Europe, run using the regional climate model COSMO-CLM with different nesting strategies, for the period 2007–2011. Precipitation data are analysed at different temporal and spatial scales with respect to gridded observational datasets (i.e., E-OBS and RADKLIM-RW) and existing reanalysis products (i.e., ERA5-Land and UERRA). The present work demonstrates that the one-step experiment tendentially outperforms the two-step one when there is no spectral nudging, providing results at different spatial and temporal scales in line with the other existing reanalysis products. However, the results can be highly model and event dependent as some different aspects might need to be considered (i.e., the nesting strategies) during the configuration phase of the climate experiments. For this reason, a clear and consolidated recommendation on this topic cannot be stated. Such a level of confidence could be achieved in future works by increasing the number of cities and events analysed. Nevertheless, these promising results represent a starting point for the optimal experimental configuration assessment, in the frame of future climate studies.

scholarly journals A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

2021 ◽  
Vol 13 (2) ◽  
pp. 164
Author(s):  
Chuyao Luo ◽  
Xutao Li ◽  
Yongliang Wen ◽  
Yunming Ye ◽  
Xiaofeng Zhang
Keyword(s):  
Short Term Memory ◽  
Weather Forecast ◽  
Vital Role ◽  
Data Sets ◽  
Short Term ◽  
Learning Techniques ◽  
Radar Echo ◽  
Hidden States ◽  
Better Than

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

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