scholarly journals On the Accuracy of Vaisala RS41 versus RS92 Upper-Air Temperature Observations

2019 ◽  
Vol 36 (4) ◽  
pp. 635-653 ◽  
Author(s):  
Bomin Sun ◽  
Tony Reale ◽  
Steven Schroeder ◽  
Michael Pettey ◽  
Ryan Smith
Keyword(s):  
Radio Occultation ◽  
Reference Data ◽  
Weather Prediction ◽  
Elevation Angle ◽  
Data Sources ◽  
Cold Bias ◽  
Stratospheric Temperature ◽  
Nwp Model ◽  
The University ◽  
Ecmwf Model

AbstractThe accuracy of Vaisala RS92 versus RS41 global radiosonde soundings, emphasizing stratospheric temperature, is assessed from January 2015 to June 2017 using ~311 500 RS92 and ~65 800 RS41 profiles and three different reference data sources. First, numerical weather prediction (NWP) model outputs are used as a transfer medium to produce relative RS92 and RS41 comparisons by analyzing observation minus NWP model background (OB–BG) and observation minus analysis (OB–AN) differences using the NOAA Climate Forecast System Reanalysis (CFSR; both comparisons) and the operational European Centre for Medium-Range Weather Forecasts (ECMWF) model (OB–AN comparison only). Second, GPS radio occultation (GPSRO) dry temperature profiles are directly compared with radiosondes, using GPSRO data from the University Corporation for Atmospheric Research (UCAR) Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and EUMETSAT Radio Occultation Meteorology (ROM) Satellite Application Facility (SAF). Third, dual launches (RS92 and RS41 suspended from the same balloon) at five sites allow direct assessments. Comparisons of RS92 versus RS41 from all reference data sources are basically consistent. These two sondes agree well with global average temperature differences <0.1–0.2 K in the lower stratosphere from 51.5 to 26.1 hPa based on global stations and the dual launches. RS41 appears to be less sensitive than RS92 to changes in solar elevation angle. This study indicates that nighttime RS92 and RS41 radiosonde temperature biases are negligible, but infers a stratospheric cold bias (<0.5 K) in the CFSR and ECMWF model data.

scholarly journals Detection and thermal description of medicanes from numerical simulation

2014 ◽  
Vol 14 (5) ◽  
pp. 1059-1070 ◽  
Author(s):  
M. A. Picornell ◽  
J. Campins ◽  
A. Jansà
Keyword(s):  
Numerical Simulation ◽  
Thermal Structure ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Small Scale ◽  
Medium Range Weather Forecast ◽  
Warm Core ◽  
Strong Winds ◽  
Nwp Model ◽  
Ecmwf Model

Abstract. Tropical-like cyclones rarely affect the Mediterranean region but they can produce strong winds and heavy precipitations. These warm-core cyclones, called MEDICANES (MEDIterranean hurriCANES), are small in size, develop over the sea and are infrequent. For these reasons, the detection and forecast of medicanes are a difficult task and many efforts have been devoted to identify them. The goals of this work are to contribute to a proper description of these structures and to develop some criteria to identify medicanes from numerical weather prediction (NWP) model outputs. To do that, existing methodologies for detecting, characterizating and tracking cyclones have been adapted to small-scale intense cyclonic perturbations. First, a mesocyclone detection and tracking algorithm has been modified to select intense cyclones. Next, the parameters that define the Hart's cyclone phase diagram are tuned and calculated to examine their thermal structure. Four well-known medicane events have been described from numerical simulation outputs of the European Centre for Medium-Range Weather Forecast (ECMWF) model. The predicted cyclones and their evolution have been validated against available observational data and numerical analyses from the literature.

Characterizing the FY-3A Microwave Temperature Sounder Using the ECMWF Model

2011 ◽  
Vol 28 (11) ◽  
pp. 1373-1389 ◽  
Author(s):  
Qifeng Lu ◽  
William Bell ◽  
Peter Bauer ◽  
Niels Bormann ◽  
Carole Peubey
Keyword(s):  
Weather Prediction ◽  
Quadratic Function ◽  
Climate Science ◽  
Brightness Temperatures ◽  
Microwave Sounding Unit ◽  
Satellite Sensors ◽  
Microwave Sounding ◽  
Data Source ◽  
Nwp Model ◽  
Ecmwf Model

Abstract China’s Feng-Yun-3A (FY-3A), launched in May 2008, is the first in a series of seven polar-orbiting meteorological satellites planned for the next decade by China. The FY-3 series is set to become an important data source for numerical weather prediction (NWP), reanalysis, and climate science. FY-3A is equipped with a microwave temperature sounding instrument (MWTS). This study reports an assessment of the MWTS instrument using the ECMWF NWP model, radiative transfer modeling, and comparisons with equivalent observations from the Advanced Microwave Sounding Unit-A (AMSU-A). The study suggests the MWTS instrument is affected by biases related to large shifts, or errors, in the frequency of the channel passbands as well as radiometer nonlinearity. The passband shifts, relative to prelaunch measurements, are 55, 39, and 33 MHz for channels 2–4, respectively. Relative to the design specification the shifts are 60, 80, and 83 MHz, with uncertainties of ±2.5 MHz. The radiometer nonlinearity results in a positive bias in measured brightness temperatures and is manifested as a quadratic function of measured scene temperatures. By correcting for both of these effects the quality of the MWTS data is improved significantly, with the standard deviations of the (observed minus simulated) differences based on short-range forecast fields reduced by 30%–50% relative to simulations using prelaunch measurements of the passband, to values close to those observed for AMSU-A-equivalent channels. The new methodology could be applied to other microwave temperature sounding instruments and illustrates the value of NWP fields for the on-orbit characterization of satellite sensors.

scholarly journals Detection and thermal description of medicanes from numerical simulation

2013 ◽  
Vol 1 (6) ◽  
pp. 7417-7447 ◽  
Author(s):  
M. A. Picornell ◽  
J. Campins ◽  
A. Jansà
Keyword(s):  
Numerical Simulation ◽  
Mediterranean Region ◽  
Thermal Structure ◽  
Weather Prediction ◽  
Small Scale ◽  
Warm Core ◽  
Detection And Tracking ◽  
Strong Winds ◽  
Nwp Model ◽  
Ecmwf Model

Abstract. Tropical-like cyclones rarely affect the Mediterranean region and they can produce strong winds and heavy precipitations. These warm-core cyclones, called MEDICANES (MEDIterranean hurriCANES), are small size, develop over the sea and are infrequent. For these reasons, the detection and forecast of medicanes are a difficult task and many efforts have been devoted to identify them. The goals of this work are to contribute to a proper description of these structures and to develop some criteria to identify medicanes from numerical weather prediction (NWP) model outputs. To do that, existing methodologies for detecting, characterizating and tracking cyclones have been adapted to small-scale intense cyclonic perturbations. First, a mesocyclone detection and tracking algorithm has been modified to select intense cyclones. Next, the parameters that define the Hart's cyclone phase diagram are tuned and calculated to examine their thermal structure. Four well-known medicane events have been described from numerical simulation outputs of the ECMWF model. The predicted cyclones and their evolution have been validated against available observational data and numerical analyses from literature.

scholarly journals Sensitivity of Forward-Modeled Bending Angles to Vertical Interpolation of Refractivity for Radio Occultation Data Assimilation

2019 ◽  
Vol 147 (1) ◽  
pp. 269-289 ◽  
Author(s):  
Shay Gilpin ◽  
Richard Anthes ◽  
Sergey Sokolovskiy
Keyword(s):  
Radio Occultation ◽  
Spline Interpolation ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Accurate Method ◽  
Forward Model ◽  
Radio Waves ◽  
Occultation Data ◽  
Nwp Model ◽  
Bending Angles

Assimilation of radio occultation (RO) observations into numerical weather prediction (NWP) models has improved forecasts, where RO is typically one of the top five observational systems contributing to forecast accuracy. By measuring the phase delay of radio waves traversing Earth’s atmosphere between global positioning system (GPS) and low-Earth orbiting satellites, RO obtains quasi-vertical profiles of bending angles (BA) of the radio waves’ trajectories. BA are the RO observation most often assimilated into NWP models, but since they are not computed or analyzed in the models, they must be computed from model data using a forward model. First, model refractivity N is computed from variables specified on the model’s vertical levels, then using the Abel integral, BA are computed from N. The forward model requires vertical differentiation of N, and accurate differentiation requires vertical interpolation of N between model levels. The interpolation results in errors, which then propagate through the forward model to produce BA errors. In this study, we investigate the sensitivity of forward-modeled BA to five different methods of vertical interpolation of N between model levels to determine a method that minimizes interpolation errors. We use RO-observed N to isolate the interpolation errors and determine an accurate method that can be applied to any NWP model. Of the five methods investigated, the log-spline interpolation reduces N and BA errors the most, regardless of the vertical resolution of the model grid.

scholarly journals Verification with Variograms

2009 ◽  
Vol 24 (4) ◽  
pp. 1102-1120 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe
Keyword(s):  
Spatial Structure ◽  
Mesoscale Model ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Spatial Structures ◽  
University Of Oklahoma ◽  
Arw Model ◽  
Nwp Model ◽  
Environmental Prediction ◽  
The University

Abstract The verification of a gridded forecast field, for example, one produced by numerical weather prediction (NWP) models, cannot be performed on a gridpoint-by-gridpoint basis; that type of approach would ignore the spatial structures present in both forecast and observation fields, leading to misinformative or noninformative verification results. A variety of methods have been proposed to acknowledge the spatial structure of the fields. Here, a method is examined that compares the two fields in terms of their variograms. Two types of variograms are examined: one examines correlation on different spatial scales and is a measure of texture; the other type of variogram is additionally sensitive to the size and location of objects in a field and can assess size and location errors. Using these variograms, the forecasts of three NWP model formulations are compared with observations/analysis, on a dataset consisting of 30 days in spring 2005. It is found that within statistical uncertainty the three formulations are comparable with one another in terms of forecasting the spatial structure of observed reflectivity fields. None, however, produce the observed structure across all scales, and all tend to overforecast the spatial extent and also forecast a smoother precipitation (reflectivity) field. A finer comparison suggests that the University of Oklahoma 2-km resolution Advanced Research Weather Research and Forecasting (WRF-ARW) model and the National Center for Atmospheric Research (NCAR) 4-km resolution WRF-ARW slightly outperform the 4.5-km WRF-Nonhydrostatic Mesoscale Model (NMM), developed by the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction (NOAA/NCEP), in terms of producing forecasts whose spatial structures are closer to that of the observed field.

scholarly journals A New Method to Correct Radiosonde Temperature Biases Using Radio Occultation Data

2017 ◽  
Vol 56 (6) ◽  
pp. 1643-1661 ◽  
Author(s):  
Jordis S. Tradowsky ◽  
Chris P. Burrows ◽  
Sean B. Healy ◽  
John R. Eyre
Keyword(s):  
Radio Occultation ◽  
Weather Prediction ◽  
A Priori ◽  
Elevation Angle ◽  
New Method ◽  
Retrieval Algorithm ◽  
Temperature Bias ◽  
Linear Version ◽  
The Difference ◽  
The Impact

AbstractA new method to estimate radiosonde temperature biases using radio occultation measurements as a reference has been developed. The bias is estimated as the difference between mean radio occultation and mean radiosonde departures from collocated profiles extracted from the Met Office global numerical weather prediction (NWP) system. Using NWP background profiles reduces the impact of spatial and temporal collocation errors. The use of NWP output also permits determination of the lowest level at which the atmosphere is sufficiently dry to analyze radio occultation dry temperature retrievals. The authors demonstrate the advantages of using a new tangent linear version of the dry temperature retrieval algorithm to propagate bending angle departures to dry temperature departures. This reduces the influence of a priori assumptions compared to a nonlinear retrieval. Radiosonde temperature biases, which depend on altitude and the solar elevation angle, are presented for five carefully chosen upper-air sites and show strong intersite differences, with biases exceeding 2 K at one of the sites. If implemented in NWP models to correct radiosonde temperature biases prior to assimilation, this method could aid the need for consistent anchor measurements in the assimilation system. The method presented here is therefore relevant to NWP centers, and the results will be of interest to the radiosonde community by providing site-specific temperature bias profiles. The new tangent linear version of the linear Abel transform and the hydrostatic integration are described in the interests of the radio occultation community.

scholarly journals Interannual variability of upper tropospheric and lower stratospheric (UTLS) region over Ganges–Brahmaputra–Meghna basin based on COSMIC GNSS RO data

2015 ◽  
Vol 8 (9) ◽  
pp. 9399-9453
Author(s):  
◽  
J. Awange ◽  
E. Forootan
Keyword(s):  
Interannual Variability ◽  
Southern Oscillation ◽  
Weather Prediction ◽  
Cold Bias ◽  
Average Height ◽  
Cosmic Radio ◽  
Thermodynamic Structure ◽  
Stratospheric Temperature ◽  
Indian Meteorological Department ◽  
Tropopause Temperature

Abstract. Poor reliability of radiosonde observational networks across South Asia imposes serious challenges in understanding climate variability and thermodynamic structure of the upper-tropospheric and lower-stratospheric (UTLS) region. The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission launched in April 2006 have overcome many observational limitations that are inherent in conventional atmospheric sounding instruments. This study investigated the interannual variability of the UTLS region over the Ganges–Brahmaputra–Meghna (GBM) basin based on COSMIC radio occultation (RO) data from August 2006 to December 2013. Detailed comparisons were also made with various different radiosonde types and Numerical Weather Prediction (NWP) products. The results indicated that Indian Meteorological Department (IMD) radiosondes performed poorly despite upgrading to newer techniques. ShangE (of China) sonde showed the best agreement with COSMIC RO data with a mean temperature difference of −0.06 °C and a standard deviation of 1.44 °C while the older version (ShangM) indicated a cold bias of 0.61 °C in the UTLS region. The inter-annual variability of temperature in the UTLS region based on COSMIC RO data indicated a clear pattern of El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) while the stratospheric temperature anomalies reflected all three major Sudden Stratospheric Warming (SSW) events since 2007. The mean tropopause temperature varied from −70 to −80 °C, with an average height of about 15.5 to 16.3 km from winter to summer, indicating a pronounced annual cycle. The annual amplitudes of tropopause were found to be in the order of 0–6 °C and 0–1.5 km from tropical south to subtropical north. The anomalies of tropopause temperature and height exhibited the patterns of ENSO and IOD exceptionally well with a correlation of 0.65 and −0.52, respectively. The temperature data from Modern-Era Retrospective Analysis for Research Application (MERRA) agreed very well with COSMIC RO data.

scholarly journals Global 3D Features of Error Variances of GPS Radio Occultation and Radiosonde Observations

2020 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Xu Xu ◽  
Xiaolei Zou
Keyword(s):  
Radio Occultation ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Error Variance ◽  
Specific Humidity ◽  
Temperature Error ◽  
Observation Error ◽  
Bending Angle ◽  
Gps Radio Occultation ◽  
Low Latitudes

Global Positioning System (GPS) radio occultation (RO) and radiosonde (RS) observations are two major types of observations assimilated in numerical weather prediction (NWP) systems. Observation error variances are required input that determines the weightings given to observations in data assimilation. This study estimates the error variances of global GPS RO refractivity and bending angle and RS temperature and humidity observations at 521 selected RS stations using the three-cornered hat method with additional ERA-Interim reanalysis and Global Forecast System forecast data available from 1 January 2016 to 31 August 2019. The global distributions, of both RO and RS observation error variances, are analyzed in terms of vertical and latitudinal variations. Error variances of RO refractivity and bending angle and RS specific humidity in the lower troposphere, such as at 850 hPa (3.5 km impact height for the bending angle), all increase with decreasing latitude. The error variances of RO refractivity and bending angle and RS specific humidity can reach about 30 N-unit2, 3 × 10−6 rad2, and 2 (g kg−1)2, respectively. There is also a good symmetry of the error variances of both RO refractivity and bending angle with respect to the equator between the Northern and Southern Hemispheres at all vertical levels. In this study, we provide the mean error variances of refractivity and bending angle in every 5°-latitude band between the equator and 60°N, as well as every interval of 10 hPa pressure or 0.2 km impact height. The RS temperature error variance distribution differs from those of refractivity, bending angle, and humidity, which, at low latitudes, are smaller (less than 1 K2) than those in the midlatitudes (more than 3 K2). In the midlatitudes, the RS temperature error variances in North America are larger than those in East Asia and Europe, which may arise from different radiosonde types among the above three regions.

scholarly journals Evaluation of Operational Monsoon Moisture Surveillance and Severe Weather Prediction Utilizing COSMIC-2/FORMOSAT-7 Radio Occultation Observations

2021 ◽  
Vol 13 (15) ◽  
pp. 2979
Author(s):  
Yu-Chun Chen ◽  
Chih-Chien Tsai ◽  
Yi-Chao Wu ◽  
An-Hsiang Wang ◽  
Chieh-Ju Wang ◽  
...  
Keyword(s):  
Numerical Model ◽  
Data Assimilation ◽  
Water Resource Management ◽  
Radio Occultation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Severe Weather ◽  
Model Experiments ◽  
Typhoon Structure ◽  
Numerical Model Experiments

Operational monsoon moisture surveillance and severe weather prediction is essential for timely water resource management and disaster risk reduction. For these purposes, this study suggests a moisture indicator using the COSMIC-2/FORMOSAT-7 radio occultation (RO) observations and evaluates numerical model experiments with RO data assimilation. The RO data quality is validated by a comparison between sampled RO profiles and nearby radiosonde profiles around Taiwan prior to the experiments. The suggested moisture indicator accurately monitors daily moisture variations in the South China Sea and the Bay of Bengal throughout the 2020 monsoon rainy season. For the numerical model experiments, the statistics of 152 moisture and rainfall forecasts for the 2020 Meiyu season in Taiwan show a neutral to slightly positive impact brought by RO data assimilation. A forecast sample with the most significant improvement reveals that both thermodynamic and dynamic fields are appropriately adjusted by model integration posterior to data assimilation. The statistics of 17 track forecasts for typhoon Hagupit (2020) also show the positive effect of RO data assimilation. A forecast sample reveals that the member with RO data assimilation simulates better typhoon structure and intensity than the member without, and the effect can be larger and faster via multi-cycle RO data assimilation.

scholarly journals Advection-Based Adjustment of Radar Measurements

2012 ◽  
Vol 140 (3) ◽  
pp. 1014-1022 ◽  
Author(s):  
Tuomo Lauri ◽  
Jarmo Koistinen ◽  
Dmitri Moisseev
Keyword(s):  
Elevation Angle ◽  
Geographical Location ◽  
Ground Level ◽  
Limited Area ◽  
Similar Amount ◽  
Radar Measurement ◽  
Wind Climatology ◽  
Radar Measurements ◽  
Nwp Model ◽  
Adjustment Scheme

When making radar-based precipitation products, a radar measurement is commonly taken to represent the geographical location vertically below the contributing volume of the measurement sample. However, when wind is present during the fall of the hydrometeors, precipitation will be displaced horizontally from the geographical location of the radar measurement. Horizontal advection will introduce discrepancies between the radar-measured and ground level precipitation fields. The significance of the adjustment depends on a variety of factors related to the characteristics of the observed precipitation as well as those of the desired end product. In this paper the authors present an advection adjustment scheme for radar precipitation observations using estimated hydrometeor trajectories obtained from the High-Resolution Limited-Area Model (HIRLAM) MB71 NWP model data. They use the method to correct the operational Finnish radar composite and evaluate the significance of precipitation advection in typical Finnish conditions. The results show that advection distances on the order of tens of kilometers are consistently observed near the edge of the composite at ranges of 100–250 km from the nearest radar, even when using a low elevation angle of 0.3°. The Finnish wind climatology suggests that approximately 15% of single radar measurement areas are lost on average when estimating ground level rainfall if no advection adjustment is applied. For the Finnish composite, area reductions of approximately 10% have been observed, while the measuring area is extended downstream by a similar amount. Advection becomes increasingly important at all ranges in snowfall with maximum distances exceeding 100 km.

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