Observational Error Estimation of FORMOSAT-3/COSMIC GPS Radio Occultation Data

2011 ◽  
Vol 139 (3) ◽  
pp. 853-865 ◽  
Author(s):  
Shu-Ya Chen ◽  
Ching-Yuang Huang ◽  
Ying-Hwa Kuo ◽  
Sergey Sokolovskiy
Keyword(s):  
Radio Occultation ◽  
Specific Humidity ◽  
Forecast Errors ◽  
Maximum Magnitude ◽  
Observational Error ◽  
Error Statistics ◽  
Gps Radio Occultation ◽  
Horizontal Gradients ◽  
Excess Phase ◽  
Observational Errors

Abstract The Global Positioning System (GPS) radio occultation (RO) technique is becoming a robust global observing system. GPS RO refractivity is typically modeled at the ray perigee point by a “local refractivity operator” in a data assimilation system. Such modeling does not take into account the horizontal gradients that affect the GPS RO refractivity. A new observable (linear excess phase), defined as an integral of the refractivity along some fixed ray path within the model domain, has been developed in earlier studies to account for the effect of horizontal gradients. In this study, the error statistics of both observables (refractivity and linear excess phase) are estimated using the GPS RO data from the Formosa Satellite 3–Constellation Observing System for Meteorology, Ionosphere and Climate (FORMOSAT-3/COSMIC) mission. The National Meteorological Center (NMC) method, which is based on lagged forecast differences, is applied for evaluation of the model forecast errors that are used for estimation of the GPS RO observational errors. Also used are Weather Research and Forecasting (WRF) model forecasts in the East Asia region at 45-km resolution for one winter month (mid-January to mid-February) and one summer month (mid-August to mid-September) in 2007. Fractional standard deviations of the observational errors of refractivity and linear excess phase both show an approximately linear decrease with height in the troposphere and a slight increase above the tropopause; their maximum magnitude is about 2.2% (2.5%) for refractivity and 1.1% (1.3%) for linear excess phase in the lowest 2 km for the winter (summer) month. An increase of both fractional observational errors near the surface in the summer month is attributed mainly to a larger amount of water vapor. The results indicate that the fractional observational error of refractivity is about twice as large as that of linear excess phase, regardless of season. The observational errors of both linear excess phase and refractivity are much less latitude dependent for summer than for winter. This difference is attributed to larger latitudinal variations of the specific humidity in winter.

scholarly journals Empirical analysis and modeling of errors of atmospheric profiles from GPS radio occultation

2011 ◽  
Vol 4 (3) ◽  
pp. 2599-2633 ◽  
Author(s):  
B. Scherllin-Pirscher ◽  
A. K. Steiner ◽  
G. Kirchengast ◽  
Y.-H. Kuo ◽  
U. Foelsche
Keyword(s):  
Geopotential Height ◽  
Radio Occultation ◽  
Error Model ◽  
Atmospheric Parameters ◽  
Observational Error ◽  
Bending Angle ◽  
Gps Radio Occultation ◽  
Error Characteristics ◽  
Precise Knowledge ◽  
Observational Errors

Abstract. The utilization of radio occultation (RO) data in atmospheric studies requires precise knowledge of error characteristics. We present results of an empirical error analysis of GPS radio occultation (RO) bending angle, refractivity, dry pressure, dry geopotential height, and dry temperature. We find very good agreement between data characteristics of different missions (CHAMP, GRACE-A, and Formosat-3/COSMIC (F3C)). In the global mean, observational errors (standard deviation from "true" profiles at mean tangent point location) agree within 0.3 % in bending angle, 0.1 % in refractivity, and 0.2 K in dry temperature at all altitude levels between 4 km and 35 km. Above ≈20 km, the observational errors show a strong seasonal dependence at high latitudes. Larger errors occur in hemispheric wintertime and are associated mainly with background data used in the retrieval process. The comparison between UCAR and WEGC results (both data centers have independent inversion processing chains) reveals different magnitudes of observational errors in atmospheric parameters, which are attributable to different background fields used. Based on the empirical error estimates, we provide a simple analytical error model for GPS RO atmospheric parameters and account for vertical, latitudinal, and seasonal variations. In the model, which spans the altitude range from 4 km to 35 km, a constant error is adopted around the tropopause region amounting to 0.8 % for bending angle, 0.35 % for refractivity, 0.15 % for dry pressure, 10 m for dry geopotential height, and 0.7 K for dry temperature. Below this region the observational error increases following an inverse height power-law and above it increases exponentially. The observational error model is the same for UCAR and WEGC data but due to somewhat different error characteristics below about 10 km and above about 20 km some parameters have to be adjusted. Overall, the observational error model is easily applicable and adjustable to individual error characteristics.

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.

Parallelization Strategies for the GPS Radio Occultation Data Assimilation with a Nonlocal Operator in the Weather Research and Forecasting Model

2014 ◽  
Vol 31 (9) ◽  
pp. 2008-2014 ◽  
Author(s):  
Xin Zhang ◽  
Ying-Hwa Kuo ◽  
Shu-Ya Chen ◽  
Xiang-Yu Huang ◽  
Ling-Feng Hsiao
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Observation Operator ◽  
Gps Radio Occultation ◽  
Phase Observation ◽  
Excess Phase ◽  
Weather Research

Abstract The nonlocal excess phase observation operator for assimilating the global positioning system (GPS) radio occultation (RO) sounding data has been proven by some research papers to produce significantly better analyses for numerical weather prediction (NWP) compared to the local refractivity observation operator. However, the high computational cost and the difficulties in parallelization associated with the nonlocal GPS RO operator deter its application in research and operational NWP practices. In this article, two strategies are designed and implemented in the data assimilation system for the Weather Research and Forecasting Model to demonstrate the capability of parallel assimilation of GPS RO profiles with the nonlocal excess phase observation operator. In particular, to solve the parallel load imbalance problem due to the uneven geographic distribution of the GPS RO observations, round-robin scheduling is adopted to distribute GPS RO observations among the processing cores to balance the workload. The wall clock time required to complete a five-iteration minimization on a demonstration Antarctic case with 106 GPS RO observations is reduced from more than 3.5 h with a single processing core to 2.5 min with 106 processing cores. These strategies present the possibility of application of the nonlocal GPS RO excess phase observation operator in operational data assimilation systems with a cutoff time limit.

The Three-Cornered Hat Method for Estimating Error Variances of Three or More Atmospheric Data Sets – Part II: Evaluating Radio Occultation and Radiosonde Observations, Global Model Forecasts, and Reanalyses

2021 ◽  
Author(s):  
Therese Rieckh ◽  
Jeremiah P. Sjoberg ◽  
Richard A. Anthes
Keyword(s):  
Radio Occultation ◽  
State Of The Art ◽  
Specific Humidity ◽  
Data Sets ◽  
Error Growth ◽  
Atmospheric Conditions ◽  
Error Statistics ◽  
Weather And Climate ◽  
Atmospheric Data ◽  
The Impact

AbstractWe apply the three-cornered hat (3CH) method to estimate refractivity, bending angle, and specific humidity error variances for a number of data sets widely used in research and/or operations: radiosondes, radio occultation (COSMIC, COSMIC-2), NCEP global forecasts, and nine reanalyses. We use a large number and combinations of data sets to obtain insights into the impact of the error correlations among different data sets that affect 3CH estimates. Error correlations may be caused by actual correlations of errors, representativeness differences, or imperfect co-location of the data sets. We show that the 3CH method discriminates among the data sets and how error statistics of observations compare to state-of-the-art reanalyses and forecasts, as well as reanalyses that do not assimilate satellite data. We explore results for October and November 2006 and 2019 over different latitudinal regions and show error growth of the NCEP forecasts with time. Because of the importance of tropospheric water vapor to weather and climate, we compare error estimates of refractivity for dry and moist atmospheric conditions.

Assimilation of GPS Radio Occultation Refractivity Data from CHAMP and SAC-C Missions over High Southern Latitudes with MM5 4DVAR

2008 ◽  
Vol 136 (8) ◽  
pp. 2923-2944 ◽  
Author(s):  
Tae-Kwon Wee ◽  
Ying-Hwa Kuo ◽  
David H. Bromwich ◽  
Andrew J. Monaghan
Keyword(s):  
Radio Occultation ◽  
Ross Sea ◽  
Mesoscale Model ◽  
Positive Impact ◽  
Extended Period ◽  
Error Reduction ◽  
Specific Humidity ◽  
Subtle Change ◽  
Gps Radio Occultation ◽  
The Impact

Abstract In this study, the GPS radio occultation (RO) data from the Challenging Minisatellite Payload (CHAMP) and Satellite de Aplicaciones Cientificas-C (SAC-C) missions are assimilated. An updated version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) four-dimensional variational data assimilation system (4DVAR) is used to assess the impact of the GPS RO data on analyses and short-range forecasts over the Antarctic. The study was performed during the period of intense cyclonic activity in the Ross Sea, 9–19 December 2001. On average 66 GPS RO soundings were assimilated daily. For the assimilation over a single 12-h period, the impact of GPS RO data was only marginally positive or near neutral, and it varied markedly from one 12-h period to another. The large case-to-case variation was attributed to the low number of GPS RO soundings and a strong dependency of forecast impact on the location of the soundings relative to the rapidly developing cyclone. Despite the moderate general impact, noticeable reduction of temperature error in the upper troposphere and lower stratosphere was found, which demonstrates the value of GPS RO data in better characterizing the tropopause. Significant error reduction was also noted in geopotential height and wind fields in the stratosphere. Those improvements indicate that early detection of the upper-level precursors for storm development is a potential benefit of GPS RO data. When the assimilation period was extended to 48 h, a considerable positive impact of GPS RO data was found. All parameters that were investigated (i.e., temperature, pressure, and specific humidity) showed the positive impact throughout the entire model atmosphere for forecasts extending up to 5 days. The impact increased in proportion to the length of the assimilation period. Although the differences in the analyses as a result of GPS RO assimilation were relatively small initially, the subtle change and subsequent nonlinear growth led to noticeable forecast improvements at longer ranges. Consequently, the positive impact of GPS RO data was more evident in longer-range (e.g., greater than 2 days) forecasts. A correlation coefficient is introduced to quantify the linear relationship between the analysis errors without GPS RO assimilation and the analysis increments induced by GPS RO assimilation. This measure shows that the growth of GPS RO–induced modifications over time is related to the prominent error reduction observed in GPS RO experiments. The measure may also be useful for understanding how cycling analysis accumulates the positive impact of GPS RO data for an extended period of assimilation.

scholarly journals Climate intercomparison of GPS radio occultation, RS90/92 radiosondes and GRUAN over 2002 to 2013

2014 ◽  
Vol 7 (11) ◽  
pp. 11735-11769
Author(s):  
F. Ladstädter ◽  
A. K. Steiner ◽  
M. Schwärz ◽  
G. Kirchengast
Keyword(s):  
Radio Occultation ◽  
Global Climate ◽  
Time Frame ◽  
Specific Humidity ◽  
Radiosonde Data ◽  
Gps Radio Occultation ◽  
Long Term Stability ◽  
High Vertical Resolution ◽  
Global Coverage

Abstract. Observations from the GPS radio occultation (GPSRO) satellite technique and from the newly established GCOS Reference Upper Air Network (GRUAN) are both candidates to serve as reference observations in the Global Climate Observing System (GCOS). Such reference observations are key to decrease existing uncertainties in upper-air climate research. There are now more than 12 years of data available from GPSRO, with the recognized properties high accuracy, global coverage, high vertical resolution, and long-term stability. These properties make GPSRO a suitable choice for comparison studies with other upper-air observational systems. The GRUAN network consists of reference radiosonde ground stations (16 at present), which adhere to the GCOS climate monitoring principles. In this study, we intercompare GPSRO temperature and humidity profiles and Vaisala RS90/92 data from the "standard" global radiosonde network over the whole 2002 to 2013 time frame. Additionally, we include the first years of GRUAN data (using Vaisala RS92), available since 2009. GPSRO profiles which occur within 3 h and 300 km of radiosonde launches are used. Very good agreement is found between all three datasets with temperature differences usually less than 0.2 K. In the stratosphere above 30 hPa, temperature differences are larger but still within 0.5 K. Day/night comparisons with GRUAN data reveal small deviations likely related to a warm bias of the radiosonde data at high altitudes, but also residual errors from the GPSRO retrieval process might play a role. Vaisala RS90/92 specific humidity exhibits a dry bias of up to 40% in the upper troposphere, with a smaller bias at lower altitudes within 15%. GRUAN shows a marked improvement in the bias characteristics, with less than 5% difference to GPSRO up to 300 hPa. GPSRO dry temperature and physical temperature are validated using radiosonde data as reference. We find that GPSRO provides valuable long-term stable reference observations with well-defined error characteristics for climate applications and for anchoring other upper-air measurements.

scholarly journals Assessment of global navigation satellite system (GNSS) radio occultation refractivity under heavy precipitation

2018 ◽  
Vol 18 (16) ◽  
pp. 11697-11708 ◽  
Author(s):  
Ramon Padullés ◽  
Estel Cardellach ◽  
Kuo-Nung Wang ◽  
Chi O. Ao ◽  
F. Joseph Turk ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Three Dimensional ◽  
Heavy Precipitation ◽  
Satellite System ◽  
Specific Humidity ◽  
Positive Bias ◽  
Actual Measurement ◽  
Excess Phase ◽  
Global Navigation Satellite ◽  
Ray Paths

Abstract. A positive bias at heights between 3 and 8 km has been observed when comparing the radio-occultation (RO)-retrieved refractivity with that of meteorological analyses and reanalyses in cases where heavy precipitation is present. The effect of precipitation in RO retrievals has been investigated as a potential cause of the bias, using precipitation measurements interpolated into the actual three-dimensional RO ray paths to calculate the excess phase induced by precipitation. The study consisted of comparing the retrievals when such extra delay is removed from the actual measurement and when it is not. The results show how precipitation itself is not the cause of the positive bias. Instead, we show that the positive bias is linked to high specific-humidity conditions regardless of precipitation. This study also shows a regional dependence of the bias. Furthermore, different analyses and reanalyses show a disagreement under high specific-humidity conditions and, in consequence, heavy precipitation.

scholarly journals Empirical analysis and modeling of errors of atmospheric profiles from GPS radio occultation

2011 ◽  
Vol 4 (9) ◽  
pp. 1875-1890 ◽  
Author(s):  
B. Scherllin-Pirscher ◽  
A. K. Steiner ◽  
G. Kirchengast ◽  
Y.-H. Kuo ◽  
U. Foelsche
Keyword(s):  
Geopotential Height ◽  
Radio Occultation ◽  
Error Model ◽  
Primary Data ◽  
Atmospheric Parameters ◽  
Observational Error ◽  
Bending Angle ◽  
Error Characteristics ◽  
Precise Knowledge ◽  
Observational Errors

Abstract. The utilization of radio occultation (RO) data in atmospheric studies requires precise knowledge of error characteristics. We present results of an empirical error analysis of GPS RO bending angle, refractivity, dry pressure, dry geopotential height, and dry temperature. We find very good agreement between data characteristics of different missions (CHAMP, GRACE-A, and Formosat-3/COSMIC (F3C)). In the global mean, observational errors (standard deviation from "true" profiles at mean tangent point location) agree within 0.3% in bending angle, 0.1% in refractivity, and 0.2 K in dry temperature at all altitude levels between 4 km and 35 km. Above 35 km the increase of the CHAMP raw bending angle observational error is more pronounced than that of GRACE-A and F3C leading to a larger observational error of about 1% at 42 km. Above ≈20 km, the observational errors show a strong seasonal dependence at high latitudes. Larger errors occur in hemispheric wintertime and are associated mainly with background data used in the retrieval process particularly under conditions when ionospheric residual is large. The comparison between UCAR and WEGC results (both data centers have independent inversion processing chains) reveals different magnitudes of observational errors in atmospheric parameters, which are attributable to different background fields used. Based on the empirical error estimates, we provide a simple analytical error model for GPS RO atmospheric parameters for the altitude range of 4 km to 35 km and up to 50 km for UCAR raw bending angle and refractivity. In the model, which accounts for vertical, latitudinal, and seasonal variations, a constant error is adopted around the tropopause region amounting to 0.8% for bending angle, 0.35% for refractivity, 0.15% for dry pressure, 10 m for dry geopotential height, and 0.7 K for dry temperature. Below this region the observational error increases following an inverse height power-law and above it increases exponentially. For bending angle and refractivity we also include formulations for error correlations in order to enable modeling of full error covariance matrices for these primary data assimilation variables. The observational error model is the same for UCAR and WEGC data but due to somewhat different error characteristics below about 10 km and above about 20 km some parameters have to be adjusted. Overall, the observational error model is easily applicable and adjustable to individual error characteristics.

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