Ground-based GPS Measurements Research Articles

Estimation of precipitable water vapor from ground based GPS measurements and Radiosonde in Southeast Asian-Pacific

Estimation of precipitable water vapor from ground based GPS measurements and Radiosonde in Southeast Asian-Pacific

An investigation of a new artificial neural network-based TEC model using ground-based GPS and COSMIC-2 measurements over low latitudes

Mapping the ionosphere over low-latitude regions with a high accuracy is a challenging task due to the complex nature of the ionospheric structure. In this study, the artificial neural network (ANN) technique was used to investigate the determination of the total electron content (TEC) in low latitude regions using measurements from 187 ground-based International global navigation satellite system (GNSS) Service (IGS) stations and the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) mission in the period from 1 October 2019 to 30 September 2021. To reduce the systematic discrepancies between the space-borne and ground-based observational systems, the ANN technique was also utilized to calibrate the COSMIC-2 derived TECs to the ground level. The RMSE of the calibrated COSMIC-2 TECs was 1.78 TECU. This, in comparison with the RMSE value of 4.10 TECU for the uncalibrated COSMIC-2 TECs, demonstrated 57% improvement. It is found that the performance of the new model was strongly associated with the value of TEC and low-latitude ionospheric structure, such as the equatorial ionization anomaly (EIA). Compared with the IRI-2016 model, the Technical University of Catalonia (UPC) rapid global ionospheric maps (GIMs) (UQRG), the Center for Orbit Determination in Europe (CODE) final GIMs (CODG), and the Chinese Academy of Sciences (CAS) final GIMs (CASG), the new model outperformed the IRI-2016 model and was competitive with the GIMs. All results suggest that the new model developed using both ground-based and space-borne measurements together with the ANN technique can model the ionospheric structure, such as the EIA. Therefore, the new model developed may become a good reference for the studies of the structure and variation of the ionosphere over low latitudes.

Radiance-based retrieval of total water vapor content from sentinel-3A OLCI NIR channels using ground-based GPS measurements

We calibrated the integrated water vapor (IWV) data retrieved from near-infrared (NIR) channels of the Ocean and Land Color Instrument (OLCI) onboard the Sentinel-3A satellite using in-situ GPS-sensed IWV observations. Unlike conventional water vapor retrieval methodologies relying upon radiative transfer code, this method utilized a regression equation to empirically estimate GPS IWV from two NIR absorption channels at 900 nm and 940 nm. The GPS IWV data were used as reference to define the relationship between the measured radiance ratio and IWV. We collected IWV data from June 1, 2016 to May 31, 2019 from 453 GPS stations situated in the inland and coastal areas of Australia. The retrieval approach was analyzed by using different sample sizes and training datasets. The evaluation results between June 1, 2019 and May 31, 2020 in Australia indicated that the algorithm could reduce the root-mean-square error (RMSE) of the operational OLCI IWV products by 12.91% from 3.114 to 2.712 mm at the O19 channel, by 10.69% to 2.781 mm at the O20 channel, and by 11.75% to 2.748 mm for the weighted mean IWV when compared with GPS reference IWV data. When compared to European Centre for Medium-Range Weather Forecasts reference IWV data, the RMSE was reduced by 12.94% from 3.154 to 2.745 mm, by 11.04% to 2.805 mm, and by 11.93% to 2.777 mm, at the O19 channel, O20 channel, and the weighted mean, respectively. The spatiotemporal performance of the OLCI IWV measurements was improved in both station-scale and daily-scale after applying the new empirical regression retrieval method. The seasonal and land-surface-type dependence of the retrieval approach was also discussed in this research.

Rainfall forecast based on GPS PWV together with meteorological parameters using neural network models

In recent years, it has been found that the Precipitable Water Vapor (PWV) time series derived from ground-based GPS measurements can be used in to forecast precipitation in different regions. However, it is inevitable to consider the impact of several meteorological parameters such as temperature, pressure, relative humidity, water vapor pressure, total cloud cover and day of year (doy) besides PWV on rainfall prediction. In order to predict the precipitation at Tehran station, two types of Artificial Neural Network (ANN), including Multi-Layer Perceptron (MLP) and Nonlinear Auto-Regressive with Exogenous Inputs (NARX) were employed based on mentioned parameters. At first, these neural networks were trained under various circumstances (i.e. with and without PWV) with the help of collocated meteorological and GPS data from years 2007–2010 and then the networks were utilized to forecast different intensities of precipitation over 2011. The results showed that deletion of PWV values from input data will reduce the precision of MLP predictions for the range of rainfalls less than 6 mm. For the range of precipitation above 3 mm, the use of PWV has a positive impact on the output of the NARX model. In addition, the effect of the length of training data on the performance of the proposed models was investigated in terms of Mean Bias Error (MBE), Root Mean Square Error (RMSE) and False Alarm Ratio (FAR) statistics. The best results in the study region were achieved from 4years trained MLP and 2years trained NARX models. Comparing the outputs of the MLP and NARX models with the Global Forecasting System (GFS) 6h forecasts as a standard meteorological forecast showed that the efficiency of the NARX model is higher than the MLP and GFS, especially in moderate and strong rainfall classes. Also, seasonal comparison of these errors showed that both models underestimate rainfall values higher than 3 mm. In almost all seasons, the underestimation of the NARX model was less than MLP. With all pros and cons, the NARX model showed greater performance than MLP for both non-rainfall and rainfall events.

Improvement of Integrated Water Vapor Products from Sentinel-3 OLCI NIR Channels Using Ground-based GPS Measurements

High-quality remote sensing water vapor monitoring plays a vital role in Earth's weather and climate observation. In this work, we developed a model to improve the accuracy of integrated water vapor (IWV) products retrieved from the Ocean and Land Color Instrument (OLCI) near-infrared (NIR) channels using ground-based GPS-derived IWV data. This algorithm uses the empirical regression equations to calibrate the official OLCI IWV products published by Sentinel-3. The reference IWV data, from June 1, 2018 to May 31, 2019, from 453 GPS sites over Australia, were employed to calculate the differential IWV data by subtracting GPS IWV data from OLCI IWV data. The OLCI IWV pixels were grouped into two categories according to the quality flag of each pixel. For each group, the relationship between the differential IWV data and the official OLCI IWV products was defined, and the model parameters were independently calculated from each season. The performance of the model was evaluated using reference IWV data from GPS and European Centre for Medium-Range Weather Forecasts (ECMWF) from June 1, 2019 to May 31, 2020 over Australia. Taking GPS IWV data as reference, the root-mean-square error (RMSE) has reduced 27.63% from 3.475 to 2.515 mm for Sentinel-3A, and 18.06% from 4.030 to 3.302 mm for Sentinel-3B. Taking ECMWF IWV data as reference, the RMSE was reduced by 25.27% from 3.490 to 2.608 mm for Sentinel-3A, and by 23.54% from 3.535 to 2.703 mm for Sentinel-3B. The improvement of OLCI IWV products was further confirmed in Mainland China, with smaller RMSE against reference IWV data from 214 GPS stations from June 1, 2019 to May 31, 2020.

Water Vapor Retrievals from Near-infrared Channels of the Advanced Medium Resolution Spectral Imager Instrument onboard the Fengyun-3D Satellite

Water vapor plays a key role in weather, climate and environmental research on local and global scales. Knowledge about atmospheric water vapor and its spatiotemporal variability is essential for climate and weather research. Because of the advantage of a unique temporal and spatial resolution, satellite observations provide global or regional water vapor distributions. The advanced Medium Resolution Spectral Imager (MERSI) instrument—that is, MERSI-II—onboard the Fengyun-3D (FY-3D) meteorological satellite, has been one of the major satellite sensors routinely providing precipitable water vapor (PWV) products to the community using near-infrared (NIR) measurements since June 2018. In this paper, the major updates related to the production of the NIR PWV products of MERSI-II are discussed for the first time. In addition, the water vapor retrieval algorithm based on the MERSI-II NIR channels is introduced and derivations are made over clear land areas, clouds, and sun-glint areas over the ocean. Finally, the status and samples of the MERSI-II PWV products are presented. The accuracy of MERSI-II PWV products is validated using ground-based GPS measurements. The results show that the accuracies of the water vapor products based on the updated MERSI-II instrument are significantly improved compared with those of MERSI, because MERSI-II provides a better channel setting and new calibration method. The root-mean-square error and relative bias of MERSI-II PWV products are typically 1.8–5.5 mm and −3.0% to −14.3%, respectively, and thus comparable with those of other global remote sensing products of the same type.

Evaluation of MODIS Near-IR water vapor product over Iran using ground-based GPS measurements

In recent years, water vapor as one of the most important greenhouse gases has played an essential role in many weather and climate processes, and it has been studied by various methods. Moderate Resolution Imaging Spectroradiometer (MODIS) sensor installed on Aqua and Terra satellites is one of the existing remote sensing tools capable of producing Precipitable Water Vapor (PWV) products. In this research, one year of ground-based observation from a network of 38 GPS stations were utilized to evaluate the accuracy of MODIS Near Infrared (Near-IR) PWV over Iran. Initially, GPS PWV estimates at three radiosonde stations (OIFM, OIII and OIMB) were evaluated. Agreement of GPS-derived PWV with respective values obtained from radiosonde in terms of bias and RMSE were 1.5 mm and 3 mm respectively. After evaluation of GPS PWV estimations in the study area, MODIS Near-IR cloud free water vapor products at the location of GPS stations were extracted and evaluated. Times series comparison between MODIS and GPS showed that both methods agree very well with a correlation coefficient of 0.95. The mean bias and RMSE of differences between these two techniques (GPS and MODIS) during one year were obtained −2.2 mm and 3.4 mm respectively. The negative value of bias indicates that MODIS Near-IR in comparison with GPS overestimates PWV in this area. In addition, it was found that the bias values and the height of the stations have a linear relation. Therefore, a linear model as a function of height was obtained to calibrate MODIS Near-IR PWV products. Results showed that the calibration with the help of the presented model increases the efficiency of MODIS Near-IR water vapor data. The Amount of bias reduction after calibration of MODIS data was obtained 2 mm.

Statistical seismo-ionospheric precursors of M7.0+ earthquakes in Circum-Pacific seismic belt by GPS TEC measurements

The Circum-Pacific seismic belt is the region heavily affected by earthquakes in the world. The relationship between earthquake (e.g., the geographic location, occurrence time, magnitude, and focal depth) and ionospheric anomalies in the belt was investigated using 100 M7.0+ earthquakes during 2006–2015. The ground-based GPS measurements and global ionosphere map (GIM) data were used for the analyses of the ionospheric variations preceding the earthquakes. The results indicated that the occurrence rate of total electron content (TEC) anomalies was proportional to the magnitude and inversely proportional to the focal depth to a certain degree, and the occurrence frequency of anomalies had a rising trend with the days getting close to the main shock. The occurrence rate of TEC anomalies in the Southern hemisphere was larger than that in the Northern hemisphere. Besides, the spatial characteristics of TEC anomalies showed that the anomalies in low-middle latitudes did not coincide with the epicenter, sometimes the anomalies were also observed in the corresponding conjugated region. However, the TEC anomalies in the high latitude usually appeared around the epicenter and within the seismogenic zone while no TEC anomalies appeared in the conjugated area. These results may have potential applications to the earthquake prediction in the Circum-Pacific seismic belt.

Precipitable water characteristics during the 2013 Colorado flood using ground-based GPS measurements

Abstract. During 9–16 September 2013, the Front Range region of Colorado experienced heavy rainfall that resulted in severe flooding. Precipitation totals for the event exceeded 450 mm, damages to public and private properties were estimated to be over USD 2 billion, and nine lives were lost. This study analyzes the characteristics of precipitable water (PW) surrounding the event using 10 years of high-resolution GPS PW data in Boulder, Colorado, which was located within the region of maximum rainfall. PW in Boulder is dominated by seasonal variability with an average summertime maximum of 36 mm. In 2013, the seasonal PW maximum extended into early September and the September monthly mean PW exceeded the 99th percentile of climatology with a value 25 % higher than the 40-year climatology. Prior to the flood, around 18:00 UTC on 8 September, PW rapidly increased from 22 to 32 mm and remained around 30 mm for the entire event as a result of the nearly saturated atmosphere. The frequency distribution of September PW for Boulder is typically normal, but in 2013 the distribution was bimodal due to a combination of above-average PW values from 1 to 15 September and much drier conditions from 16 to 30 September. The above-normal, near-saturation PW values during the flood were the result of large-scale moisture transport into Colorado from the Tropical Eastern Pacific and the Gulf of Mexico. This moisture transport was the product of a stagnating cutoff low over the southwestern United States working in conjunction with an anticyclone located over the southeastern United States. A blocking ridge located over the Canadian Rocky Mountains kept both of the synoptic features in place over the course of several days, which helped to provide continuous moisture to the storm, thus enhancing the accumulated precipitation totals.

Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data

The knowledge of biomass stocks in tropical forests is critical for climate change and ecosystem services studies. This research was conducted in a tropical rain forest located near the city of Libreville (the capital of Gabon), in the Akanda Peninsula. The forest cover was stratified in terms of mature, secondary and mangrove forests using Landsat-ETM data. A field inventory was conducted to measure the required basic forest parameters and estimate the aboveground biomass (AGB) and carbon over the different forest classes. The Shuttle Radar Topography Mission (SRTM) data were used in combination with ground-based GPS measurements to derive forest heights. Finally, the relationships between the estimated heights and AGB were established and validated. Highest biomass stocks were found in the mature stands (223 ± 37 MgC/ha), followed by the secondary forests (116 ± 17 MgC/ha) and finally the mangrove forests (36 ± 19 MgC/ha). Strong relationships were found between AGB and forest heights (R2 > 0.85).

Evaluation of Precipitable Water Vapor from Four Satellite Products and Four Reanalysis Datasets against GPS Measurements on the Southern Tibetan Plateau

AbstractThe southern Tibetan Plateau (STP) is the region in which water vapor passes from South Asia into the Tibetan Plateau (TP). The accuracy of precipitable water vapor (PWV) modeling for this region depends strongly on the quality of the available estimates of water vapor advection and the parameterization of land evaporation models. While climate simulation is frequently improved by assimilating relevant satellite and reanalysis products, this requires an understanding of the accuracy of these products. In this study, PWV data from MODIS infrared and near-infrared measurements, AIRS Level-2 and Level-3, MERRA, ERA-Interim, JRA-55, and NCEP final reanalysis (NCEP-Final) are evaluated against ground-based GPS measurements at nine stations over the STP, which covers the summer monsoon season from 2007 to 2013. The MODIS infrared product is shown to underestimate water vapor levels by more than 20% (1.84 mm), while the MODIS near-infrared product overestimates them by over 40% (3.52 mm). The AIRS PWV product appears to be most useful for constructing high-resolution and high-quality PWV datasets over the TP; particularly the AIRS Level-2 product has a relatively low bias (0.48 mm) and RMSE (1.83 mm) and correlates strongly with the GPS measurements (R = 0.90). The four reanalysis datasets exhibit similar performance in terms of their correlation coefficients (R = 0.87–0.90), bias (0.72–1.49 mm), and RMSE (2.19–2.35 mm). The key finding is that all the reanalyses have positive biases along the PWV seasonal cycle, which is linked to the well-known wet bias over the TP of current climate models.

Morphology of equatorial plasma bubbles during low and high solar activity years over Indian sector

In the present study, slant total electron content (STEC) data computed from ground based GPS measurements over Hyderabad (Geog. Lat. 17.41° N, geog. long. 78.55° E, mag. lat. 08.81° N) and two close stations at Bangalore (Geog. Lat. 13.02°/13.03° N, geog. long. 77.57°/77.51° E, mag. lat. 04.53°/04.55° N) in Indian region during 2007–2012, have been used to study the occurrences and characteristics of equatorial plasma bubbles (EPBs). The analysis found maximum EPB occurrences during the equinoctial months and minimum during the December solstice throughout 2007–2012 except during the solar minimum years in 2007–2009. During 2007–2009, the maximum EPB occurrences were observed in June solstice which could not be predicted by the model proposed by Tsunoda (J. Geophys. Res., 90:447–456, 1985). The equinox maximum in EPB occurrences for high solar activity years could be caused by the vertical F-layer drift due to pre-reversal electric field (PRE), and expected to be maximum when day-night terminator aligns with the magnetic meridian i.e. during the equinox months whereas maximum occurrences during the solstice months of solar minimum could be caused by the seed perturbation in plasma density induced by gravity waves from tropospheric origins. Generally EPB occurrences are found to be more prominent during nighttime hours (2000–2400 hours) than the daytime hours. Peak in EPB occurrences is in early night for high solar activity years whereas same is late night for low solar activity. The day and nighttime EPB occurrences have been analyzed and found to vary in accordance with solar activity with an annual correlation coefficient ( $R$ ) of ∼0.99 with $\mathrm{F}_{10.7}~\mbox{cm}$ solar Flux. Additionally, solar activity influence on EPB occurrences is seasonal dependent with a maximum influence during the equinox season ( $R=0.88$ ) and a minimum during winter season ( $R =0.73$ ). The solar activity influences on EPB occurrences are found in agreement with the previous works reported in the Brazilian, African-Asian and Pacific longitudes sector but different than that in Atlantic sector.

Which are the highest peaks in the US Arctic? Fodar settles the debate

Abstract. Though an outstanding achievement for their time, the United States Geological Survey (USGS) topographic maps of the eastern Alaskan Arctic nonetheless contain significant errors, and in this paper we address one of them. Specifically, USGS maps of different scale made in the late 1950s alternate between Mt. Chamberlin and Mt. Isto as the tallest peak in the US Arctic. Given that many of the peaks here are close in height and covered with glaciers, recent climate change may also have changed their height and their order. We resolved these questions using fodar, a new airborne photogrammetric technique that utilizes structure-from-motion (SfM) software and requires no ground control, and validated it using GPS measurements on the peaks as well as airborne lidar. Here we show that Mt. Chamberlin is currently the third tallest peak and that the order and elevations of the five tallest mountains in the US Arctic are Mt. Isto (2735.6 m), Mt. Hubley (2717.6 m), Mt. Chamberlin (2712.3 m), Mt. Michelson (2698.1 m), and an unnamed peak (2694.9 m); these heights are relative to the NAVD88 GEOID12A vertical datum. We find that it is indeed plausible that this ranking has changed over time and may continue to change as summit glaciers continue to shrink, though Mt. Isto will remain the highest under current climate trends. Mt. Isto is also over 100 m taller than the highest peak in Arctic Canada, making it the highest peak in the North American Arctic. Fodar elevations compared to within a few centimeters of our ground-based GPS measurements of the peaks made a few days later and our complete validation assessment indicates a measurement uncertainty of better than ±20 cm (95 % RMSE). By analyzing time series of fodar maps, we were able to detect topographic change on the centimeter level on these steep slopes, indicating that fodar can be used to measure mountain snow packs for water resource availability or avalanche danger, glacier volume change, and slope subsidence, as well as many other applications of benefit to society. Compared to lidar, the current state-of-the-art airborne topographic mapping, we found this SfM technique as accurate, more useful scientifically, and significantly less expensive, suggesting that fodar is a disruptive innovation that will enjoy widespread usage in the future.

Precipitable water vapor and its relationship with the Standardized Precipitation Index: ground-based GPS measurements and reanalysis data

Monthly means of ground-based GPS measurements of precipitable water vapor (PWV) from six stations in the USA covering the period January 2007–December 2012 are analyzed to investigate their usefulness for monitoring meteorological wet/dry spells. For this purpose, the relationship between PWV and the Standardized Precipitation Index (SPI) on 1-month timescale is investigated. The SPI time series at grid points close to the stations are computed using gridded precipitation records from the NOAA Climate Prediction Center (CPC) unified precipitation dataset (January 1948–April 2012). GPS measurements are first verified against PWV data taken from the latest ECMWF reanalysis ERA-Interim; these PWV reanalysis data, which extend back to 1979, are then used jointly with CPC precipitation to compute precipitation efficiency (PE), defined as the percentage of total water vapor content that falls onto the surface as measurable precipitation in a given time period. The overall results suggest that (i) PWV time series are dominated by the seasonal cycle with maximum values during summer months, (ii) the comparison between GPS and ERA-Interim PWV monthly data shows good agreement with differences less than 4 mm, (iii) at all stations and for almost all months, PWV is only poorly correlated with recorded precipitation and the SPI, while PE correlates highly with the SPI, providing an estimate of the water availability at a given location and useful information on wet/dry spell occurrence, and (iv) long data records would allow, for each month of the year, the identification of PE thresholds associated with different SPI classes that, in turn, have potential for forecasting meteorological wet/dry spells. Thus, it is through PE that ground-based GPS measurements appear of relevance for assessing wet/dry spells, although there is not a direct relationship between PWV and SPI.

Interannual variability patterns of the world’s total column water content: Amazon River basin

Global trend patterns of yearly mean total column water (TCW) from the European Centre for Medium Range Weather Forecasts (ECMWF) 20th century atmosphere model ERA-20CM (1900–2009) and the currently state-of-the-art reanalysis ERA-Interim (1979–2012) show common features of statistically significant upward trends. Of particular interest appears a pronounced regional dipole pattern of interannual climate variability over the South American continent particularly evident in ERA-Interim data. The trend dipole affects two distinct areas: the Andean Amazon basin and the Northeast Brazil. The target regions are characterized by rising and decreasing water content associated with water vapor convergence (divergence) and upward (downward) mass fluxes, respectively. As expected, local water vapor feedback due to local surface temperature change does to not fully explain this TCW trend dipole; other mechanisms may play a role in establishing the observed feature such as moisture transports and monsoon variability in the last decade. The observed trends of the normalized difference vegetation index (NDVI) during the period 1982–2005 show an increasing greenness that coincides with the moistening of the atmospheric column in the Amazon basin. These results are substantiated by two single-station ground-based GPS measurements of TCW vapor (TCWV) from the two target regions.

Ground-Based GPS Measurements of Precipitable Water Vapor and Their Usefulness for Hydrological Applications

Half-hourly ground-based GPS measurements of precipitable water vapor (PWV) from January 2009 to December 2012 are analyzed to investigate their potential for hydrological applications at basin level. In particular, the usefulness of these high temporal resolution data for monitoring extreme weather conditions, such as floods and meteorological dry/wet spells, is discussed. Two sample GPS stations in U.S. from the SoumiNet network are considered that have rather continuous data for the last four years and a few missing values. Results suggest that: (i) A flood event is characterized by an anomalous increase of PWV accompanied by a sudden lowering of surface pressure; (ii) Precipitable water tendency (DPWV) becomes increasingly small moving from half-hour to monthly time scale, but not negligible compared with both the moisture flux divergence div(Q) and the imbalance between actual evapotranspiration and precipitation (E–P), especially during spring and fall; (iii) GPS observations, jointly with other meteorological data, can provide an accurate estimate of the imbalance (E–P) that is of interest for drought assessment, and of the terrestrial water storage rate of change that is known to be difficult to measure; (iv) the availability of on-site precipitation observations allows the computation of precipitation efficiency, which is a key parameter for estimating the water availability in a given area and monitoring dry/wet spells. It appears that for a comprehensive monitoring of a river basin, a GPS network that encloses the area of concern, equipped with meteorological ground sensors, is suitable and desirable.

Ground-based GPS measurements: time behavior from half-hour to years

Ground-based GPS and weather stations time series for the period 2010–2012 of precipitable water vapor (PWV), relative humidity (RH), and surface temperature (T) of half-hourly resolution are analyzed to demonstrate their value for dynamical analyses and weather forecasting. Three sample stations in the USA from the SoumiNet network are considered, which have rather continuous data for the last 3 years and a few missing values. Results for the three stations reveal the following features: (1) PWV time behavior is dominated by the annual cycle superimposed on high-frequency fluctuations with missing daily cycle, indicating a prevailing large-scale transport source of precipitable water at these sites; (2) RH is characterized by the daily cycle and high-frequency variability, while the annual cycle is missing; (3) T mainly varies following the annual and diurnal cycles; and (4) all variables show similar scaling properties of their variance spectra, S(f) ∼ f–β, with a high-frequency regime of red noise type scaling (β ∼ 2) up to a day and long-term persistence beyond a week (β ∼ 0.5), with a week-long frequency interval of transition. Detrended fluctuation analysis of relative humidity indicates a clear long-term persistence scaling covering more than three decades. Implications of these findings on weather forecasting and climate modeling are discussed.

Using the IRI, the MAGIC model, and the co-located ground-based GPS receivers to study ionospheric solar eclipse and storm signatures on July 22, 2009

The longest total solar eclipse in the 21st century occurred in Southeast Asia on 22 July 2009 from 00:55 to 04:15 UT, and was accompanied by a moderate magnetic storm starting at 03:00 UT with a Dst reduction of −78 nT at 07:00 UT. In this study, we use the ionospheric reference model IRI, the data assimilation model MAGIC, and ground-based GPS receivers to simulate and examine the ionospheric solar eclipse and geomagnetic storm signatures in Taiwan and Japan. Cross-comparisons between the two model results and observations show that IRI fails to simulate the two signatures while MAGIC partially reproduces the storm features. It is essential to include ground-based GPS measurements to improve the IRI performance.

Synergistic use of multitemporal RAMP, ICESat and GPS to construct an accurate DEM of the Larsemann Hills region, Antarctica

An enhanced digital elevation model (DEM) of the Larsemann Hills region, east Antarctica, is constructed synergistically by using highly accurate ground-based GPS measurements, satellite-derived laser altimetry (GLAS/ICESat) and Radarsat Antarctic Mapping Project (RAMPv2) DEM-based point elevation dataset. Our DEM has a vertical accuracy of about 1.5 times better than RAMPv2 DEM and seven times better than GLAS/ICESAT-based DEM. The accuracy is improved by validating the RAMPv2 DEM elevation by supplementing with GLAS/ICESat and DGPS survey data, when compared to that of DEM constructed by using GLAS/ICESat or RAMPv2 alone. With the use of accurate GPS data as ground control points reference elevations, the DEM extracted is much more accurate with least mean RMSE of 34.5m than that constructed by using a combination of GLAS/ICESat and RAMPv2 as true reference. The newly constructed DEM 7 achieves highest accuracy with the least average elevation difference of 0.27m calculated using 46 ground reference points. Available DEMs of Antarctic region generated by using radar altimetry and the Antarctic Digital Database indicate elevation variations in the range of 50–100m, which necessitates the generation of local DEM and its validation by using ground truth. This is our first attempt of fusing multi-temporal, multi-sensor and multi-source elevation data to generate a DEM of any part of Antarctica, in order to address the ice elevation change to infer the ice mass balance. Our approach focuses on the strengths of each elevation data source to produce an accurate DEM.

Evaluation of AIRS Precipitable Water Vapor against Ground-based GPS Measurements over the Tibetan Plateau and Its Surroundings

The Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite provides the estimate of precipitable water vapor (PWV), which can be assimilated into numerical prediction models to improve precipitation forecasts. In this study, the AIRS retrieval of PWV is evaluated against ground-based GPS measurements at 24 stations over the Tibetan Plateau (TP) and its surroundings. First, the PWV estimates from the GPS delay signals at these stations are improved by applying a new scheme for computing the water-vapor-weighted mean atmosphere temperature, a key parameter in the GPS PWV retrieval. Compared with a traditional retrieval, this revision can improve the PWD estimate by up to 6% in some cases. Second, the newly retrieved PWV data are used to evaluate the AIRS product. Prior to the evaluation, an elevation correction is made to these GPS PWV data to account