PPP with integer ambiguity resolution for GPS and Galileo using satellite products from different analysis centers

Integer ambiguity resolution is the key for achieving the highest accuracy with Precise Point Positioning (PPP) and for significantly reducing the convergence time. Unfortunately, due to hardware phase biases originating from the satellites and receiver, fixing the phase ambiguities to their correct integer number is difficult in PPP. Nowadays, various institutions and analysis centers of the International GNSS Service (IGS) provide satellite products (orbits, clocks, biases) based on different strategies, which allow PPP with integer ambiguity resolution (PPP-AR) for GPS and Galileo. We present the theoretical background and practical application of the satellite products from CNES, CODE, SGG, and TUG. They are tested in combined GPS and Galileo PPP-AR solutions calculated using our in-house software raPPPid. The numerical results show that the choice of satellite product has an influence on the convergence time of the fixed solution. The satellite product of CODE performs better than the following, in the given order: SGGCODE, SGGGFZ, TUG, CNES, and SGGCNES. After the convergence period, a similar level of accuracy is achieved with all these products. With these satellite products and observations with an interval of 30 s, a mean convergence time of about 6 min to centimeter-level 2D positioning is achieved. Using high-rate observations and an observation interval of 1 s, this period can be reduced to a few minutes and, in the best case, just one minute.

Investigating the performance of AEOLUS L2A products over Europe with EARLINET ground-based lidars

<p>In this study, we present a comparison of the AEOLUS satellite L2A product with the retrievals of the ground-based lidar systems of EARLINET (European Aerosol Research Lidar Network), part the European Research Infrastructure for the observation of Aerosol, Clouds and Trace Gases (ACTRIS). Dedicated ground‐based measurements during AEOLUS overpasses have been performed among the 29 member stations since the beginning of the mission, however, we have included only the stations that have gathered a significant number of collocations in the analysis. The satellite timeseries we deployed covers the period 2019-2020 that correspond to the best available version of the satellite processing algorithms. We harvest the collocations using the following spacio-temporal criteria. Only overpasses that fall within a radius less than 100km around the station are included. Using this criterion, the AEOLUS L2A climatology is generated per station independently of the ground-based measurements. To isolate collocated data we reject all AEOLUS data with a time interval between the overpass and the central time of the ground-based measurement that is greater than 3 hours. The ground based lidar climatology is also computed per station. AEOLUS L2A products include aerosol extinction coefficient profiles and aerosol co-polar backscatter coefficient profiles from circularly polarized light emission. While the extinction profiles are directly comparable with the ground-based lidars, this is not the case for the backscatter profiles since AEOLUS cannot measure the cross polar component of the aerosol backscatter. The co-polar backscatter is close to the total backscatter only in the absence of depolarizing scatterers such as dust, pollen, volcanic ash, and cirrus ice crystals. Ground-based measurements are divided in two categories for the evaluation depending on whether aerosol depolarization measurements have been performed. If the particle linear depolarization ratio (PLDR) is available, it can be applied to convert the lidar total backscatter to an AOLUS-like co-polar backscatter coefficient. This category is applied for the direct evaluation of the satellite product. Cases that lack PLDR information assist to quantify the uncertainties introduced by using the AEOLUS co-polar backscatter as a substitute for the total backscatter. The analysis includes both an indirect climatological comparison and a direct collocation comparison between the ground based and satellite datasets. Via the collocation comparison, random and systematic uncertainties in the satellite product are identified and quantified. A climatological comparison can show the potential of AEOLUS to capture annual cycles despite its intrinsic random errors. In the future, the analysis will be further supported with auxiliary data such as sunphotometer measurements, aerosol classification flags, modeled backward trajectories, and satellite cloud fraction data.</p>

Evaluation of OMI NO2 Vertical Columns Using MAX-DOAS Observations over Mexico City

Nitrogen dioxide (NO2) is a gas pollutant that can be measured from space and several operational products are now available from instruments on-board of satellite-based platforms. There are still, however, many unknowns about the accuracy of these products under different viewing and surface conditions since ground-based observations are generally scarce. This is particularly the case of high-altitude sub-tropical megacities such as the Mexico City Metropolitan Area (MCMA). In this study, we use more than five years of data from four ground-based MAX-DOAS instruments distributed within the MCMA in order to evaluate the DOMINO product from the Ozone Monitoring Instrument (OMI) on board the Aura satellite. We compare OMI against each MAX-DOAS site independently using the vertical column densities (VCDs) reported by each instrument. The VCDs are also compared after smoothing the MAX-DOAS profiles with the a priori and the Averaging Kernels of the satellite product. We obtain an overall correlation coefficient (R) of 0.6 that does not improve significantly after the smoothing is applied. However, the slopes in the linear regressions for the individual sites improve when applying the smoothing from 0.36 to 0.62 at UNAM, from 0.26 to 0.49 at Acatlán, from 0.78 to 1.23 at Vallejo, and from 0.50 to 0.97 at the Cuautitlán station. The large differences observed between the OMI and MAX-DOAS VCDs are attributed to a reduced sensitivity of the satellite product near the surface and the large aerosol loading typically present within the mixed layer of the MCMA. This may also contribute to a slight overestimation of the VCDs from the MAX-DOAS measurements that presents a total error (random + systematic) of about 20%. As a result of this comparison, we find that OMI retrievals are on average 56% lower than the MAX-DOAS without any correction. The near-surface concentrations are estimated from the lowest layers of the MAX-DOAS retrievals and these compare well with surface measurements from in situ analyzers operated at the co-located air quality monitoring stations. The diurnal variability for each station is analyzed and discussed in relation to their location within the city.

Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling

Precipitation is essential for modeling the hydrologic behavior of watersheds. There exist multiple precipitation products of different sources and precision. We evaluate the influence of different precipitation product on model parameters and streamflow predictive uncertainty using a soil water assessment tool (SWAT) model for a forest dominated catchment in India. We used IMD (gridded rainfall dataset), TRMM (satellite product), bias-corrected TRMM (corrected satellite product) and NCEP-CFSR (reanalysis dataset) over a period from 1998–2012 for simulating streamflow. The precipitation analysis using statistical measures revealed that the TRMM and CFSR data slightly overestimate rainfall compared to the ground-based IMD data. However, the TRMM estimates improved, applying a bias correction. The Nash–Sutcliffe (and R2) values for TRMM, TRMMbias and CFSR, are 0.58 (0.62), 0.62 (0.63) and 0.52 (0.54), respectively at model calibrated with IMD data (Scenario A). The models of each precipitation product (Scenario B) yielded Nash–Sutcliffe (and R2) values 0.71 (0.76), 0.74 (0.78) and 0.76 (0.77) for TRMM, TRMMbias and CFSR datasets, respectively. Thus, the hydrological model-based evaluation revealed that the model calibration with individual rainfall data as input showed increased accuracy in the streamflow simulation. IMD and TRMM forced models to perform better in capturing the streamflow simulations than the CFSR reanalysis-driven model. Overall, our results showed that TRMM data after proper correction could be a good alternative for ground observations for driving hydrological models.

Aircraft observations since the 1990s reveal increases of tropospheric ozone at multiple locations across the Northern Hemisphere

Tropospheric ozone is an important greenhouse gas, is detrimental to human health and crop and ecosystem productivity, and controls the oxidizing capacity of the troposphere. Because of its high spatial and temporal variability and limited observations, quantifying net tropospheric ozone changes across the Northern Hemisphere on time scales of two decades had not been possible. Here, we show, using newly available observations from an extensive commercial aircraft monitoring network, that tropospheric ozone has increased above 11 regions of the Northern Hemisphere since the mid-1990s, consistent with the OMI/MLS satellite product. The net result of shifting anthropogenic ozone precursor emissions has led to an increase of ozone and its radiative forcing above all 11 study regions of the Northern Hemisphere, despite NOx emission reductions at midlatitudes.

Spatially resolved evaluation of Earth system models with satellite column averaged CO₂

Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) showed large uncertainties in simulating atmospheric CO2 concentrations. By comparing the simulations with satellite observations, in this study we find slight improvements in the ESMs participating in the new Phase 6 (CMIP6) compared to CMIP5. We utilize the Earth System Model Evaluation Tool (ESMValTool) to evaluate emission driven CMIP5 and CMIP6 simulations with satellite data of column-average CO2 mole fractions (XCO2). The satellite data are a combined data product covering the period 2003­–2014 based on the SCIAMACHY/ENVISAT (2003–2012) and TANSO-FTS/GOSAT (2009–­2014) instruments. In this study the Observations for Model Intercomparisons Project (Obs4MIPs) format data product version 3 (O4Mv3) with a spatial resolution of 5° × 5° and monthly time resolution has been used. Comparisons of XCO2 time series show a large spread among the model ensembles both in CMIP5 and CMIP6, with differences in the absolute concentrations of up to approximately 20 ppmv relative to the satellite observations. The multi-model mean has a bias of approximately +10 and +2 ppmv in CMIP5 and CMIP6, respectively. The derived atmospheric XCO2 growth rate (GR) is typically slightly overestimated in the models, with a slightly better average and lower spread for CMIP6. The interannual variability of the growth rate is well reproduced in the multi-model mean. All models capture the expected increase of the seasonal cycle amplitude (SCA) with increasing latitude, but most models underestimate the SCA. Most models from both ensembles show a positive trend of the SCA over the period 2003–2014, i.e. an increase of the SCA with XCO2, similar to in situ ground-based measurements. In contrast, the combined satellite product shows a negative trend over this period. Any SCA derived from sampled data can only be considered an effective SCA, as sampling can remove the peaks or troughs. This negative trend can be reproduced by the models when sampling them as the observations. The average effective SCA in the models is higher when using the SCIAMACHY/ENVISAT instead of the TANSO-FTS/GOSAT mean data coverage mask, overall leading to a negative trend over the full period similar to the combined satellite product. Models with a larger difference in the average effective SCA between the two coverages also show a stronger trend reversal. Therefore, this trend reversal in the satellite data is due to sampling characteristics, more specifically the different data coverage of the two satellites that can be reproduced by the models if sampled the same way. Overall, the CMIP6 ensemble shows better agreement with the satellite data in all considered quantities (XCO2, GR, SCA and trend in SCA), with the biggest improvement in the mean XCO2 content of the atmosphere. This study shows that the availability of column-integral CO2 from satellite provides a promising new way to evaluate the performance of Earth System Models on a global scale, complementing existing studies that are based on in situ measurements from single ground-based stations.