Evaluation of Modeling Water-Vapor-Weighted Mean Tropospheric Temperature for GNSS-Integrated Water Vapor Estimates in Brazil

AbstractMeteorological application of Global Navigation Satellite System (GNSS) data over Brazil has increased significantly in recent years, motivated by the significant amount of investment from research agencies. Several projects have, among their principal objectives, the monitoring of humidity over Brazilian territory. These research projects require integrated water vapor (IWV) values with maximum quality, and, accordingly, appropriate data from the installed meteorological stations, together with the GNSS antennas, have been used. The model that is applied to estimate the water-vapor-weighted mean tropospheric temperature (Tm) is a source of uncertainty in the estimate of IWV values using the ground-based GNSS receivers in Brazil. Two global models and one algorithm for Tm, developed through the use of radiosondes, numerical weather prediction products, and 40-yr ECMWF Re-Analysis (ERA-40), as well as two regional models, were evaluated using a dataset of ~78 000 radiosonde profiles collected at 22 stations in Brazil during a 12-yr period (1999–2010). The regional models (denoted the Brazilian and regional models) were developed with the use of multivariate statistical analysis using ~90 000 radiosonde profiles launched at 12 stations over a 32-yr period (1961–93). The main conclusion is that the Brazilian model and two global models exhibit similar performance if the complete dataset and the entire period are taken into consideration. However, for seasonal and local variations of the Tm values, the Brazilian model was better than the other two models for most stations. The Tm values from ERA-40 present no bias, but their scatter is larger than that in the other models.

Download Full-text

Comparison of different machine learning approaches for tropospheric profiling based on COSMIC-2 data

Earth Planets and Space ◽

10.1186/s40623-021-01548-4 ◽

2021 ◽

Vol 73 (1) ◽

Author(s):

Elżbieta Lasota

Keyword(s):

Machine Learning ◽

Water Vapor ◽

Meteorological Data ◽

Satellite System ◽

Tropospheric Temperature ◽

Learning Approaches ◽

Vapor Pressures ◽

Data Set ◽

Artificial Neural Network Ann ◽

Global Navigation Satellite

Abstract Precise and reliable information on the tropospheric temperature and water vapor profiles play a key role in weather and climate studies. Among the sensors supporting the observations of the troposphere, one can distinguish the Global Navigation Satellite System Radio Occultation (RO) technique, which provides accurate and high-quality meteorological profiles. However, external knowledge about temperature is essential to estimate other physical atmospheric parameters. To overcome this constraint, I trained and evaluated four different machine learning models comprising Artificial Neural Network (ANN) and Random Forest regression algorithms, where no auxiliary meteorological data is needed. To develop the models, I employed 150,000 globally distributed (45°S–45°N) RO profiles between October 2019 and December 2020. Input vectors consisted of bending angle or refractivity profiles from the Formosa Satellite-7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 mission together with the month, hour, and latitude of the RO event. While temperature, pressure, and water vapor profiles derived from the modern ERA5 reanalysis and interpolated to the RO location served as the models’ targets. Evaluation on the testing data set revealed a good agreement between all model outputs and ERA5 targets, where slightly better statistics were noted for ANN and refractivity inputs. Vertically averaged root mean square error (RMSE) did not exceed 1.7 K for the temperature and reached around 1.4 hPa and 0.45 hPa for the total and water vapor pressures. Additional validation with 477 co-located radiosonde observations and the operational one-dimensional variational product showed slightly larger discrepancies with the mean RMSE of around 1.9 K, 1.9 hPa, and 0.5 hPa for the temperature, pressure, and water vapor, respectively. Graphical Abstract

Download Full-text

A NOVEL PROCEDURE FOR GENERATION OF SAR-DERIVED ZTD MAPS FOR WEATHER PREDICTION: APPLICATION TO SOUTH AFRICA USE CASE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2021-405-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 405-410

Author(s):

M. E. Molinari ◽

M. Manzoni ◽

N. Petrushevsky ◽

A. M. Guarnieri ◽

G. Venuti ◽

...

Keyword(s):

Water Vapor ◽

Weather Prediction ◽

Satellite System ◽

Cloud Services ◽

Technology Readiness ◽

Technology Readiness Level ◽

Readiness Level ◽

Sar Imagery ◽

Global Navigation Satellite ◽

Fast Procedure

Abstract. The knowledge of tropospheric water vapor distribution can significantly improve the accuracy of Numerical Weather Prediction (NWP) models. The present work proposes an automatic and fast procedure for generating reliable water vapor products from the synergic use of Sentinel-1 Synthetic Aperture Radar (SAR) imagery and Global Navigation Satellite System (GNSS) observations. Moreover, a compression method able to drastically reduce, without significant accuracy loss, the water vapor dataset dimension has been implemented to facilitate the sharing through cloud services. The activities have been carried in the EU H2020 TWIGA project framework, aimed at providing water vapor maps at Technology Readiness Level 7.

Download Full-text

An Adaptive Non-Uniform Vertical Stratification Method for Troposphere Water Vapor Tomography

Remote Sensing ◽

10.3390/rs13193818 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3818

Author(s):

Hao Wang ◽

Nan Ding ◽

Wenyuan Zhang

Keyword(s):

Water Vapor ◽

Exponential Distribution ◽

Weather Prediction ◽

Satellite System ◽

Vertical Stratification ◽

Reference Station ◽

Specific Calculation ◽

Satellite Positioning ◽

Stratification Method ◽

Global Navigation Satellite

Global Navigation Satellite System (GNSS) water vapor tomography provides a four-dimensional (4-D) distribution of water vapor in the atmosphere for weather monitoring. It has developed into a widely used technique in numerical weather prediction (NWP). Vertical stratification is essential in discretizing the tomographic region. Traditional discretization methods divide the tomographic area into regular voxels with an equal height interval, which ignores the dynamic exponential distribution of water vapor. In recent years, non-uniform stratification methods have been widely validated by tomographic experiments. However, such experiments have not proposed a specific calculation method for stratification thickness. Therefore, in this paper, we introduced an adaptive non-uniform stratification method that follows the exponential distribution of water vapor in the tomographic region and presented the process of iterative calculation to acquire the optimal stratification interval. The proposed approach was applied based on the exponential decreasing trend in water vapor with increasing altitude. Moreover, it could adaptively calculate the interval of stratification height according to water vapor content. The tomographic experiments were performed using Global Positioning System (GPS) data from 19 ground-based stations in the Hong Kong Satellite Positioning Reference Station Network (SatRef) from 1 to 31 August 2019. The results indicated that, compared to the traditional stratification method, the root mean square error derived from the proposed approach was reduced by 0.26 g/m3. Additionally, severe weather can negatively affect the accuracy of the tomographic results. The results also showed that the accuracy of the tomographic results was reduced with increasing altitude. Moreover, the performance of the tomographic water vapor fields below 3000 m was improved by the proposed approach.

Download Full-text

Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

Advances in Atmospheric Sciences ◽

10.1007/s00376-021-0326-5 ◽

2021 ◽

Author(s):

Magnus Lindskog ◽

Adam Dybbroe ◽

Roger Randriamampianina

Keyword(s):

Data Assimilation ◽

Positive Impact ◽

Weather Prediction ◽

Implementation Process ◽

Satellite System ◽

Limited Area ◽

Global Navigation Satellite ◽

Microwave Radiances ◽

Data Assimilation System ◽

Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

Download Full-text

Water Vapor Retrievals from Spectral Direct Irradiance Measured with an EKO MS-711 Spectroradiometer—Intercomparison with Other Techniques

Remote Sensing ◽

10.3390/rs13030350 ◽

2021 ◽

Vol 13 (3) ◽

pp. 350

Author(s):

Rosa Delia García ◽

Emilio Cuevas ◽

Victoria Eugenia Cachorro ◽

Omaira E. García ◽

África Barreto ◽

...

Keyword(s):

Water Vapor ◽

Power Plants ◽

Precipitable Water ◽

Satellite System ◽

Infrared Spectrometry ◽

Radiative Balance ◽

Direct Normal Irradiance ◽

Routine Basis ◽

Solar Power Plants ◽

Global Navigation Satellite

Precipitable water vapor retrievals are of major importance for assessing and understanding atmospheric radiative balance and solar radiation resources. On that basis, this study presents the first PWV values measured with a novel EKO MS-711 grating spectroradiometer from direct normal irradiance in the spectral range between 930 and 960 nm at the Izaña Observatory (IZO, Spain) between April and December 2019. The expanded uncertainty of PWV (UPWV) was theoretically evaluated using the Monte-Carlo method, obtaining an averaged value of 0.37 ± 0.11 mm. The estimated uncertainty presents a clear dependence on PWV. For PWV ≤ 5 mm (62% of the data), the mean UPWV is 0.31 ± 0.07 mm, while for PWV > 5 mm (38% of the data) is 0.47 ± 0.08 mm. In addition, the EKO PWV retrievals were comprehensively compared against the PWV measurements from several reference techniques available at IZO, including meteorological radiosondes, Global Navigation Satellite System (GNSS), CIMEL-AERONET sun photometer and Fourier Transform Infrared spectrometry (FTIR). The EKO PWV values closely align with the above mentioned different techniques, providing a mean bias and standard deviation of −0.30 ± 0.89 mm, 0.02 ± 0.68 mm, −0.57 ± 0.68 mm, and 0.33 ± 0.59 mm, with respect to the RS92, GNSS, FTIR and CIMEL-AERONET, respectively. According to the theoretical analysis, MB decreases when comparing values for PWV > 5 mm, leading to a PWV MB between −0.45 mm (EKO vs. FTIR), and 0.11 mm (EKO vs. CIMEL-AERONET). These results confirm that the EKO MS-711 spectroradiometer is precise enough to provide reliable PWV data on a routine basis and, as a result, can complement existing ground-based PWV observations. The implementation of PWV measurements in a spectroradiometer increases the capabilities of these types of instruments to simultaneously obtain key parameters used in certain applications such as monitoring solar power plants performance.

Download Full-text

Assessment of Sampling Effects on Various Satellite-Derived Integrated Water Vapor Datasets Using GPS Measurements in Germany as Reference

Remote Sensing ◽

10.3390/rs12071170 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1170 ◽

Cited By ~ 3

Author(s):

Cintia Carbajal Henken ◽

Lisa Dirks ◽

Sandra Steinke ◽

Hannes Diedrich ◽

Thomas August ◽

...

Keyword(s):

Water Vapor ◽

Pairwise Comparison ◽

Weather Conditions ◽

Satellite System ◽

High Temporal Resolution ◽

Data Sets ◽

Frequency Distributions ◽

Temporal Sampling ◽

Global Navigation Satellite ◽

Satellite Instruments

Passive imagers on polar-orbiting satellites provide long-term, accurate integrated water vapor (IWV) data sets. However, these climatologies are affected by sampling biases. In Germany, a dense Global Navigation Satellite System network provides accurate IWV measurements not limited by weather conditions and with high temporal resolution. Therefore, they serve as a reference to assess the quality and sampling issues of IWV products from multiple satellite instruments that show different orbital and instrument characteristics. A direct pairwise comparison between one year of IWV data from GPS and satellite instruments reveals overall biases (in kg/m 2 ) of 1.77, 1.36, 1.11, and −0.31 for IASI, MIRS, MODIS, and MODIS-FUB, respectively. Computed monthly means show similar behaviors. No significant impact of averaging time and the low temporal sampling on aggregated satellite IWV data is found, mostly related to the noisy weather conditions in the German domain. In combination with SEVIRI cloud coverage, a change of shape of IWV frequency distributions towards a bi-modal distribution and loss of high IWV values are observed when limiting cases to daytime and clear sky. Overall, sampling affects mean IWV values only marginally, which are rather dominated by the overall retrieval bias, but can lead to significant changes in IWV frequency distributions.

Download Full-text

The FengYun-3C radio occultation sounder GNOS: a review of the mission and its early results and science applications

Atmospheric Measurement Techniques ◽

10.5194/amt-11-5797-2018 ◽

2018 ◽

Vol 11 (10) ◽

pp. 5797-5811 ◽

Cited By ~ 11

Author(s):

Yueqiang Sun ◽

Weihua Bai ◽

Congliang Liu ◽

Yan Liu ◽

Qifei Du ◽

...

Keyword(s):

Quality Evaluation ◽

Radio Occultation ◽

Global Climate ◽

Weather Prediction ◽

Satellite System ◽

Navigation Satellite ◽

Neutral Atmosphere ◽

Scientific Applications ◽

Early Results ◽

Global Navigation Satellite

Abstract. The Global Navigation Satellite System (GNSS) Occultation Sounder (GNOS) is one of the new-generation payloads on board the Chinese FengYun 3 (FY-3) series of operational meteorological satellites for sounding the Earth's neutral atmosphere and ionosphere. FY-3C GNOS, on board the FY-3 series C satellite launched in September 2013, was designed to acquire setting and rising radio occultation (RO) data by using GNSS signals from both the Chinese BeiDou Navigation Satellite System (BDS) and the US Global Positioning System (GPS). So far, the GNOS measurements and atmospheric and ionospheric data products have been validated and evaluated and then been used for atmosphere- and ionosphere-related scientific applications. This paper reviews the FY-3C GNOS instrument, RO data processing, data quality evaluation, and preliminary research applications according to the state-of-the-art status of the FY-3C GNOS mission and related publications. The reviewed data validation and application results demonstrate that the FY-3C GNOS mission can provide accurate and precise atmospheric and ionospheric GNSS (i.e., GPS and BDS) RO profiles for numerical weather prediction (NWP), global climate monitoring (GCM), and space weather research (SWR). The performance of the FY-3C GNOS product quality evaluation and scientific applications establishes confidence that the GNOS data from the series of FY-3 satellites will provide important contributions to NWP, GCM, and SWR scientific communities.

Download Full-text

Validation of the Water Vapor Profiles of the Raman Lidar at the Maïdo Observatory (Reunion Island) Calibrated with Global Navigation Satellite System Integrated Water Vapor

Atmosphere ◽

10.3390/atmos10110713 ◽

2019 ◽

Vol 10 (11) ◽

pp. 713 ◽

Cited By ~ 1

Author(s):

Hélène Vérèmes ◽

Guillaume Payen ◽

Philippe Keckhut ◽

Valentin Duflot ◽

Jean-Luc Baray ◽

...

Keyword(s):

Water Vapor ◽

Global Navigation Satellite System ◽

Satellite System ◽

Navigation Satellite ◽

Reunion Island ◽

Raman Lidar ◽

Réunion Island ◽

Global Navigation ◽

Global Navigation Satellite ◽

Good Agreement

The Maïdo high-altitude observatory located in Reunion Island (21 ∘ S, 55.5 ∘ E) is equipped with the Lidar1200, an innovative Raman lidar designed to measure the water vapor mixing ratio in the troposphere and the lower stratosphere, to perform long-term survey and processes studies in the vicinity of the tropopause. The calibration methodology is based on a GNSS (Global Navigation Satellite System) IWV (Integrated Water Vapor) dataset. The lidar water vapor measurements from November 2013 to October 2015 have been calibrated according to this methodology and used to evaluate the performance of the lidar. The 2-year operation shows that the calibration uncertainty using the GNSS technique is in good agreement with the calibration derived using radiosondes. During the MORGANE (Maïdo ObservatoRy Gaz and Aerosols NDACC Experiment) campaign (Reunion Island, May 2015), CFH (Cryogenic Frost point Hygrometer) radiosonde and Raman lidar profiles are compared and show good agreement up to 22 km asl; no significant biases are detected and mean differences are smaller than 9% up to 22 km asl.

Download Full-text

A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography

Sensors ◽

10.3390/s18082526 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2526 ◽

Cited By ~ 8

Author(s):

Fei Yang ◽

Jiming Guo ◽

Junbo Shi ◽

Lv Zhou ◽

Yi Xu ◽

...

Keyword(s):

Water Vapor ◽

Precipitable Water ◽

Absolute Error ◽

Satellite System ◽

Observation Data ◽

Radiosonde Station ◽

Satellite Signal ◽

Comparative Results ◽

Global Navigation Satellite ◽

Vapor Distribution

Water vapor is an important driving factor in the related weather processes in the troposphere, and its temporal-spatial distribution and change are crucial to the formation of cloud and rainfall. Global Navigation Satellite System (GNSS) water vapor tomography, which can reconstruct the water vapor distribution using GNSS observation data, plays an increasingly important role in GNSS meteorology. In this paper, a method to improve the distribution of observations in GNSS water vapor tomography is proposed to overcome the problem of the relatively concentrated distribution of observations, enable satellite signal rays to penetrate more tomographic voxels, and improve the issue of overabundance of zero elements in a tomographic matrix. Numerical results indicate that the accuracy of the water vapor tomography is improved by the proposed method when the slant water vapor calculated by GAMIT is used as a reference. Comparative results of precipitable water vapor (PWV) and water vapor density (WVD) profiles from radiosonde station data indicate that the proposed method is superior to the conventional method in terms of the mean absolute error (MAE), standard deviations (STD), and root-mean-square error (RMS). Further discussion shows that the ill-condition of tomographic equation and the richness of data in the tomographic model need to be discussed separately.

Download Full-text

IWV retrieval from ship-borne GNSS receiver in the framework of the MAP-IO project

10.5194/ems2021-172 ◽

2021 ◽

Author(s):

Pierre Bosser ◽

Joël Van Ballen ◽

Olivier Bousquet

Keyword(s):

Marine Biology ◽

Weather Prediction ◽

Vertical Component ◽

Satellite System ◽

Sea Surface Height ◽

Sea Surface ◽

Gnss Receiver ◽

Height Determination ◽

Global Navigation Satellite ◽

Height Model

In the framework of the research project &#8220;Marion Dufresne Atmospheric Program &#8211; Indian Ocean&#8221; (MAP-IO), which is aiming at collecting long-term atmospheric and marine biology observations in the under-instrumented Indian and Austral Oceans, a Global Navigation Satellite System (GNSS) receiver was installed on the research vessel (RV) Marion Dufresne in October 2020 to describe, and monitor, global moisture changes in these areas. GNSS raw data are recorded continuously and used to retrieve integrated water vapor contents (IWV) along the RV route.After a data quality check that confirmed that a wise choice of location of the GNSS antenna on the RV is crucial to avoid mask, signal reflection and interference from other instruments that may degrade IWV retrieval, a first assessment of the GNSS analysis performances was carried out by comparing the vertical component of the estimated positions to sea surface height model. The differences are on the order of 20 to 30 cm; they are consistent with both the error budget for sea surface height determination using GNSS and the sea surface height model formal errors.An evaluation of GNSS-derived IWV was conducted using IWV estimates from the ECMWF fifth ReAnalysis (ERA5) and ground-based GNSS reference stations located nearby the tracks of RV Marion Dufresne. Preliminary analyses show encouraging results with a mean root mean square error of ~2-3&#160;kg&#160;m-2 between ERA5 and GNSS-derived IWV. The use of ultra-rapid GNSS orbit and clock product was also investigated to assess the performance of near real-time GNSS-derived IWV estimation for numerical weather prediction purposes.

Download Full-text