Satellite Climate Research Articles

Evaluation of meteorological drought and its impact on crop yield over Afar region, northeast Ethiopia

Drought is a natural hazard caused by a prolonged precipitation deficit that cannot meet human, livestock, and environmental demands. In Ethiopia, frequent and severe droughts increasingly affect the socio-economic and environmental sectors. This study evaluates the current and future projected meteorological drought and its impact on crop yield over the Afar region in northeast Ethiopia. We used surface stations, satellite climate estimates, downscaled atmospheric reanalysis, and regional climate model datasets. We evaluated the occurrence of drought using the Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) calculated at 3-month and 12-month time scales. The drought vs. regional cereal yield is correlated to explain yield variability in the region. Results showed that more intense droughts were analyzed in 1984, 1985, 2002, 2008, 2009, 2010, 2015, and 2016. Among these years, 1984, 2002, 2008, 2009, and 2015 were the driest years across all locations in the study area. The regression of SPI and SPEI with yield showed that the indices significantly explained (r2 = 0.56 for SPI and 0.18 for SPEI) the observed yield variation. Spatially, more intense drought prevails over the northern, northwestern, and southwestern parts of Afar, where these parts are more prone to severe drought. The projected drought pattern showed increases in the intensity and frequency of drought in the middle and end of the century. The findings of this study are helpful for stakeholders working on drought mitigation in the region. Keywords: Climate dataset, meteorological drought indices, drought projection, pastoral community.

Speed and accuracy investigations of neural network algorithms for ionospheric modelling at an equatorial region

Neural networks are very efficient tools for modeling, including ionospheric modeling. The training algorithm is important for achieving the optimum performance of the trained network. This research is therefore meant to evaluate and compare the performances of ten neural network training algorithms based on their prediction accuracies, and the duration/times taken by each of the algorithms to establish the optimum result. The neural networks were trained using electron density measurements by Radio Occultation (RO) technique from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) satellites. Data for the period 2006 through 2021 was used. The networks were trained using about 2.9 million data points collected from the Kano region, Nigeria (5-degree rectangular region around geographic: 12.00° N, 8.59° E) after performing data quality control. The training algorithms considered in the work include: Bayesian Regularization (BR); Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFG); Conjugate Gradient with Powell/Beale (CGB); Fletcher-Reeves Conjugate Gradient (CGF); Gradient descent with momentum and adaptive learning rate (GDX); Levenberg Marquardt (LM); One Step Secant (OSS); Polak-Ribiére Conjugate Gradient (CGP); Resilient Backpropagation (RP); and Scaled Conjugate Gradient (SCG). The results showed that the BR and the LM algorithms gave the best performances in minimizing the errors of prediction (the mean RMSEs are respectively 112 and 114 ×103 electrons/cm3), but the RP algorithm, which came third in terms of accuracy, was significantly faster than both the LM and BR algorithms. The worst-performing algorithm in terms of accuracy was the GDX algorithm, although it was the fastest algorithm. The BFG algorithm was the worst-performing algorithm in terms of a combination of speed and accuracy. The developed neural network model was validated using ionosonde electron density measurements obtained from Ilorin, Nigeria (geographic: 8.5° N, 4.5° E; geomagnetic: 1.8° S). A comparison of the neural network, the NeQuick, and the IRI model predictions relative to the ionosonde measurements indicate that the neural network model was the best-performing model; the NN model predictions minimized the mean absolute errors (MAEs) in ∼44% of 399 ionosonde profiles investigated, the IRI model did so in ∼32%, and the NeQuick did so in ∼24%. The MAEs of the NeQuick however exhibited the best (least) variance. In overall, the NN model gave the least (best) mean of the MAEs (∼73 × 103 cm−3), compared to ∼82 × 103 cm−3 given by both the NeQuick and the IRI models, further supporting the idea that neural networks are excellent for present-day ionospheric modeling.

Optical Characterization of Coastal Waters with Atmospheric Correction Errors: Insights from SGLI and AERONET-OC

This study identifies the characteristics of water regions with negative normalized water-leaving radiance (nLw(λ)) values in the satellite observations of the Second-generation Global Imager (SGLI) sensor aboard the Global Change Observation Mission–Climate (GCOM-C) satellite. SGLI Level-2 data, along with atmospheric and in-water optical properties measured by the sun photometers in the AErosol RObotic NETwork-Ocean Color (AERONET-OC) from 26 sites globally, are utilized in this study. The focus is particularly on Tokyo Bay and the Ariake Sea, semi-enclosed water regions in Japan where previous research has pointed out the occurrence of negative nLw(λ) values due to atmospheric correction with SGLI. The study examines the temporal changes in atmospheric and in-water optical properties in these two regions, and identifies the characteristics of regions prone to negative nLw(λ) values due to atmospheric correction by comparing the optical properties of these regions with those of 24 other AERONET-OC sites. The time series results of nLw(λ) and the single-scattering albedo (ω(λ)) obtained by the sun photometers at the two sites in Tokyo Bay and Ariake Sea, along with SGLI nLw(λ), indicate the occurrence of negative values in SGLI nLw(λ) in blue band regions, which are mainly attributed to the inflow of absorptive aerosols. However, these negative values are not entirely explained by ω(λ) at 443 nm alone. Additionally, a comparison of in situ nLw(λ) measurements in Tokyo Bay and the Ariake Sea with nLw(λ) values obtained from 24 other AERONET-OC sites, as well as the inherent optical properties (IOPs) estimated through the Quasi-Analytical Algorithm version 5 (QAA_v5), identified five sites—Gulf of Riga, Long Island Sound, Lake Vanern, the Tokyo Bay, and Ariake Sea—as regions where negative nLw(λ) values are more likely to occur. These regions also tend to have lower nLw(λ) values at shorter wavelengths. Furthermore, relatively high light absorption by phytoplankton and colored dissolved organic matter, plus non-algal particles, was confirmed in these regions. This occurs because atmospheric correction processing excessively subtracts aerosol light scattering due to the influence of aerosol absorption, increasing the probability of the occurrence of negative nLw(λ) values. Based on the analysis of atmospheric and in-water optical measurements derived from AERONET-OC in this study, it was found that negative nLw(λ) values due to atmospheric correction are more likely to occur in water regions characterized by both the presence of absorptive aerosols in the atmosphere and high light absorption by in-water substances.

Does the subsidiary's climate risk matter? Evidence from the stock price crash

Combining satellite climate data with the subsidiaries’ geographical locations of listed firms in China, we document a significant positive relation between subsidiary-level climate risk exposure and future stock price crash risk of conglomerate. The results are stronger for firms with higher CEO performance pressure, higher subsidiary climate risk management costs and weaker external governance. Our findings shed light on the economic consequences of climate risks at the subsidiary level.

Studying the Aerosol Effect on Deep Convective Clouds over the Global Oceans by Applying Machine Learning Techniques on Long-Term Satellite Observation

Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models.

Application of wetPf2 Data for Investigating Characteristics of Temperature and Humidity of Air Masses over Paracel and Spratly Islands

This article uses data from the second-generation Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) satellites (wetPf2) to study the temperature and humidity properties of the air masses over Paracel and Spratly Islands in the Vietnam East Sea (South China Sea). The satellite observational data were validated with the radiosonde data from three stations in Vietnam: Hanoi, Danang, and Ho Chi Minh City. Subsequently, the wetPf2 data are used to analyze the characteristics of temperature and relative humidity variations of the air masses over the Paracel and Spratly regions. Results show that the mean error of the satellite observational data for temperature ranges from −0.06°C to −0.02°C, with standard deviations ranging from 0.73°C to 1.04°C. The mean error of relative humidity fluctuates between 11.6% and 12.5%, with standard deviations ranging from 15.1% to 19.1%. The values are reasonable and comparable to those in previous studies. Seasonal variations of temperature and humidity show that the air mass over the Paracel Islands exhibits a larger annual temperature with an annual variation of approximately 5.0°C, significantly higher than the value of 2.2°C in the air mass over the Spratly Islands. The difference may be due to the greater influence of continental and seasonal wind systems in the northern region. Within both air masses, the annual temperature variation in the boundary layer is much larger than that in the free atmosphere. Annual relative humidity variation is higher in summer and autumn than in winter and spring. The significant changes in the relative humidity with height during winter and no significant change of the relative humidity with height during summer may be related to the important role of strong convective activity carrying moist air upward to higher atmospheric levels during the summer time.

Safe drinking water supply under extreme climate events: evidence from four urban sprawl communities

ABSTRACT In this study, the impacts of climate variability/change on water supply in three urban sprawl communities were examined. Using historical satellite climate datasets and social surveys, the study assesses the water stress during different seasons in urban sprawl communities. The primary data was gathered through structured questionnaires and focused group discussions (FGDs) in various communities throughout the study area. The stress of accessing drinking water was evaluated in different seasons and during climate extreme events. The correlation analysis was used to further examine the relationship between specific variables and people's perceptions of major observed climate change as they induce water stress. The results from local people's perception of climate change impacts on safe drinking water supply reflect meteorological analysis, which indicates that the mean minimum temperature has increased, 1.0° <Tmin> 1.3°C in the urban sprawl communities. The results indicate that age and time living in the neighbourhood have a significant influence on how people perceive and understand climate change as they induce water stress. These have resulted in much stress for women, who are forced to walk a long distance to fetch drinking water for the households, during the extremely dry seasons.

The development of a global LAI and FAPAR product using GCOM-C/SGLI data

The Japan Aerospace Exploration Agency (JAXA) launched the Global Change Observation Mission - Climate (GCOM-C) satellite on December 23rd, 2017. As a part of the standard products of JAXA’s GCOM-C satellite, we developed the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product. In comparison to other global LAI and FAPAR products by the National Aeronautics and Space Administration (NASA) and the Copernicus Global Land Service (CGLS), our products have the following advantageous features: high spatial and temporal resolution (250 m and daily); the retrieval algorithm (look-up-table) is based on “the Forest Light Environmental Simulator” (FLiES) which is a full 3-D canopy radiative transfer model as well as a vegetation landscape model and a global forest landscape map created for this study. The algorithm and ancillary data sets are completely independent of the existing products that are more or less influenced by the MODIS LAI/FAPAR, providing an alternate and independent source of LAI and FAPAR. The dataset has been created since April 2018 and is freely available on JAXA’s G-Portal site (https://gportal.jaxa.jp/gpr). As a result of accuracy assessment with in-situ data, the Root Mean Square Errors (RMSEs) for total LAI and FAPAR were 0.776 and 0.112, respectively. We also compared our LAI and FAPAR estimates with three existing global datasets (PROBA-V/OLCI, MODIS and VIIRS product). The spatial distribution of LAI and FAPAR showed different characteristics among the datasets although their temporal trends were similar. The differences in estimation results among datasets due to differences in the retrieval algorithms, base map (or land cover data) and input data need to be evaluated in the future.

Climatology of the global summer monsoon rainy seasons: Revisited from a high-resolution satellite climate data record

Reliable information on the monsoon rainfall at finer spatiotemporal scales has always been crucial for applications in hydrometeorology, water resource management, disaster prevention, and assessment of numerical model outputs. Here, the spatial-temporal characteristics of the summer monsoon rainy seasons worldwide are revisited using a global, high-resolution, bias-corrected satellite rainfall climate data record. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides 0.25° rainfall estimates for the latitude band 60°S–60°N from 1983 to the near present. The global monsoon regimes are defined based on the annual rainfall range and the summer rainfall ratio. The onset, peak, and withdrawal of the rainy season are then determined using a concise yet pertinent rainfall parameter with universal objective criteria.Overall, six major summer monsoon regimes of the globe, the Asian Monsoon, Indonesian–Australian monsoon, North African monsoon, South African monsoon, North American and South American monsoon, are all reasonably represented by PERSIANN-CDR. The progression patterns of climatological onset, peak, and withdrawal phases of these monsoons are consistent with those reported by previous global and regional monsoon studies. Furthermore, compared with other coarser resolution satellite rainfall estimates such as the Climate Prediction Center Merged Analysis of Precipitation (CMAP), the present result is clearly in better agreement with that obtained from ground-based observations since it allows the determination of more detailed information associated with local-scale factors such as the sharp land-sea contrast, local surface heating, and topography effects. However, some “spots”, particularly over complex topography lacking ground truth validations, are revealed to have rainy season characteristics that differ substantially from those over the surrounding areas and have not been fully described by published literature. These distinct features need to be confirmed by further regional monsoon studies.

A Bispectral Approach for Destriping and Denoising the Sea Surface Temperature from SGLI Thermal Infrared Data

Abstract Stripe noise is a common issue in sea surface temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multidetector radiometers. We developed a bispectral filter (BSF) to reduce the stripe noise. The BSF is a Gaussian filter and an optimal estimation method for the differences between the data obtained at the split window. A kernel function based on the physical processes of radiative transfer has made it possible to reduce stripe and random noise in retrieved SSTs without degrading the spatial resolution or generating bias. The Second-Generation Global Imager (SGLI) is an optical sensor on board the Global Change Observation Mission–Climate (GCOM-C) satellite. We applied the BSF to SGLI data and validated the retrieved SSTs. The validation results demonstrate the effectiveness of BSF, which reduced stripe noise in the retrieved SGLI SSTs without blurring SST fronts. It also improved the accuracy of the SSTs by about 0.04 K (about 13%) in the robust standard deviation. Significance Statement This method reduces stripe noise and improves the accuracy of SST data with minimal compromise of spatial resolution. The method assumes the relationship between the brightness temperature and the brightness temperature difference in the split window based on the physical background of atmospheric radiative transfer. The physical background of the data provides an easy solution to a complex problem. Although destriping generally requires a complex algorithm, our approach is based on a simple Gaussian filter and is easy to implement.

Analyzing Sensitive Aerosol Regimes and Active Geolocations of Aerosol Effects on Deep Convective Clouds over the Global Oceans by Using Long-Term Operational Satellite Observations

Long-term satellite climate data records of aerosol and cloud along with meteorological reanalysis data have been used to study the aerosol effects on deep convective clouds (DCCs) over the global oceans from a climatology perspective. Our focus is on identifying sensitive aerosol regimes and active geolocations of the aerosol effects on DCCs by using statistical analyses on long-term averaged aerosol and cloud variables. We found the aerosol effect tends to manifest relatively easily on the long-term mean values of observed cloud microphysical variables (e.g., cloud particle size and ice water amount) compared to observed cloud macrophysical variables (e.g., cloud cover and cloud top height). An increase of aerosol loading tends to increase DCC particle size and ice water amount in the tropical convergence zones but decrease them in the subtropical subsidence regions. The aerosol effect on the cloud microphysical variables is also likely to manifest over the northwestern Pacific Ocean and central and eastern subtropical Pacific Ocean. The aerosol effect manifested on the microphysical cloud variables may also propagate to cloud cover but weakly to cloud top height since the latter is more susceptible to the influence of cloud dynamical and thermodynamic processes. Our results, based on the long-term averaged operational satellite observation, are valuable for the evaluation and improvement of aerosol-cloud interactions in global climate models.

An Empirical Model of the Ionospheric Sporadic E Layer Based on GNSS Radio Occultation Data

AbstractThe intense plasma irregularities within the ionospheric sporadic E (Es) layers at 90–130 km altitude have a significant impact on radio communications and navigation systems. As a result, the modeling of the Es layer is very important for the accuracy, reliability, and further applications of modern real‐time global navigation satellite system precise point positioning. In this study, we have constructed an empirical model of the Es layer using the multivariable nonlinear least‐squares‐fitting method, based on the S4max from Constellation Observing System for Meteorology, Ionosphere, and Climate satellite radio occultation measurements in the period 2006–2014. The model can describe the climatology of the intensity of Es layers as a function of altitude, latitude, longitude, universal time, and day of year. To validate the model, the outputs of the model were compared with ionosonde data. The correlation coefficients of the hourly foEs and the daily maximum foEs between the ground‐based ionosonde observations and model outputs at Beijing are 0.52 and 0.68, respectively. The model can give a global climatology of the intensity of Es layers and the seasonal variations of Es layers, although the Es layers during the summer are highly variable and difficult to accurately predict. The outputs of the model can be implemented in comprehensive models for a description of the climatology of Es layers and provide relatively accurate information about the global variation of Es layers.

A New Global Ionospheric Electron Density Model Based on Grid Modeling Method

AbstractBased on nearly 4.6 million radio occultation (RO) ionospheric profile data from Constellation Observing System for Meteorology, Ionosphere, and Climate satellites in 2006–2020, a global three‐dimensional ionospheric electron density model was constructed with a new concept. The global 3D ionosphere structure was divided into total 338,661 grids with longitude intervals of 10°, latitude intervals of 2°, and height intervals of 5 km. On each grid, the electron density is modeled as a function of solar activity, geomagnetic activity, local time, and season variation using 21 coefficients. Then all grid models are combined to form a global ionospheric model. This method makes full use of all ionospheric electron density data. The spatial resolution of the model is high with 10° in longitude, 2° in latitude, and 5 km in height, which can effectively model the fine ionospheric spatial structure like the longitudinal wavenumber‐4 structure in low latitudes. The model also takes into account the influence of both solar and geomagnetic activities on the ionosphere. At the altitude below the F2 peak, the RO electron density observation suffers from an artificial meridional maximum between the double peaks of the equatorial ionization anomaly, referred hereafter as the three‐peak error. By combining our model with the International Reference Ionospheric electron density results of the E layer below 140 km, the problem of three‐peak error is effectively solved. Compared the with other data sources such as the ZH01 and the ROCSAT‐1, the modeling ability of model in fine spatial structure is verified.

Quality assessment of Second-generation Global Imager (SGLI)-observed cloud properties using SKYNET surface observation data

Abstract. The Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission – Climate (GCOM-C) satellite, launched on 23 December 2017, observes various geophysical parameters with the aim of better understanding the global climate system. As part of that aim, SGLI has great potential to unravel several uncertainties related to clouds by providing new cloud products along with several other atmospheric products related to cloud climatology, including aerosol products from polarization channels. However, very little is known about the quality of the SGLI cloud products. This study uses data about clouds and global irradiances observed from the Earth's surface using a sky radiometer and a pyranometer, respectively, to understand the quality of the two most fundamental cloud properties – cloud optical depth (COD) and cloud-particle effective radius (CER) – of both water and ice clouds. The SGLI-observed COD agrees well with values observed from the surface, although it agrees better for water clouds than for ice clouds, while the SGLI-observed CER exhibits poorer agreement than does the COD, with SGLI values being generally higher than the sky radiometer values. These comparisons between the SGLI and sky radiometer cloud properties are found to differ for different cloud types of both the water and ice cloud phases and different solar and satellite viewing angles by agreeing better for relatively uniform and flat cloud type and for relatively low solar zenith angle. Analyses of SGLI-observed reflectance functions and values calculated by assuming plane-parallel cloud layers suggest that SGLI-retrieved cloud properties can have biases in the solar and satellite viewing angles, similar to other satellite sensors including the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, it is found that the SGLI-observed cloud properties reproduce global irradiances quite satisfactorily for both water and ice clouds by resembling several important features of the COD comparison, such as better agreement for water clouds than for ice clouds and the tendency to underestimate (resp. overestimate) the COD in SGLI observations for optically thick (resp. thin) clouds.

A regional ionospheric assimilation study with GPS and COSMIC measurements using a 3D-var algorithm (IDA4D)

The Ionospheric Data Assimilation Four-Dimension (IDA4D) is, developed by Bust et al. (2007) , a continuous time and three-dimension variational (3D-Var) algorithm that can optimally estimate the ionosphere from a model and measured data. In this study, we utilized three different data types into IDA4D for a regional ionosphere estimation: slant total electron contents (STEC) obtained from a Global Positioning System (GPS) network, NmF2 (peak electron density of the F2 layer) and STEC from Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) satellite. These multiple type data were assimilated into a background model, the International Reference Ionosphere (IRI – 2016). To evaluate the effect of each data type, the assimilation was performed on the following data combinations (cases): (1) GPS-STEC’s only; (2) GPS-STEC’s and NmF2′s from COSMIC; (3) GPS-STEC’s and COSMIC-STEC’s; and (4) all three data types. For each case computed foF2′s (F2 layer critical frequency) from IDA4D were compared with measured values from five ionosondes (I-Cheon, Jeju, Okinawa, Kokubunji, and Wakkanai) in the region of Korea and vicinities for the test periods of March, June, September and December in 2015. The comparison shows that computed foF2′s for all cases have higher correlation coefficients (CC) and less root mean square errors (RMSE) from measured values than IRI estimated values. In Cases 2, 3 and 4, IDA4D performance progressively improved in areas where GPS measurements were not covered but COSMIC STEC and NmF2 data were available. Furthermore, the IDA4D assimilation yields better results for electron density profiles and TEC values in comparison with observed values than the IRI model does. Thus, our study suggests that the IDA4D model with multiple data types can provide a reliable estimation of the regional ionosphere over the Korean Peninsula and vicinities.

Stability of CubeSat Clocks and Their Impacts on GNSS Radio Occultation

Global Navigation Satellite Systems’ radio occultation (GNSS-RO) provides the upper troposphere-lower stratosphere (UTLS) vertical atmospheric profiles that are complementing radiosonde and reanalysis data. Such data are employed in the numerical weather prediction (NWP) models used to forecast global weather as well as in climate change studies. Typically, GNSS-RO operates by remotely sensing the bending angles of an occulting GNSS signal measured by larger low Earth orbit (LEO) satellites. However, these satellites are faced with complexities in their design and costs. CubeSats, on the other hand, are emerging small and cheap satellites; the low prices of building them and the advancements in their components make them favorable for the GNSS-RO. In order to be compatible with GNSS-RO requirements, the clocks of the onboard receivers that are estimated through the precise orbit determination (POD) should have short-term stabilities. This is essential to correctly time tag the excess phase observations used in the derivation of the GNSS-RO UTLS atmospheric profiles. In this study, the stabilities of estimated clocks of a set of CubeSats launched for GNSS-RO in the Spire Global constellation are rigorously analysed and evaluated in comparison to the ultra-stable oscillators (USOs) onboard the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) satellites. Methods for improving their clock stabilities are proposed and tested. The results (i) show improvement of the estimated clocks at the level of several microseconds, which increases their short-term stabilities, (ii) indicate that the quality of the frequency oscillator plays a dominant role in CubeSats’ clock instabilities, and (iii) show that CubeSats’ derived UTLS (i.e., tropopause) atmospheric profiles are comparable to those of COSMIC-2 products and in situ radiosonde observations, which provided external validation products. Different comparisons confirm that CubeSats, even those with unstable onboard clocks, provide high-quality RO profiles, comparable to those of COSMIC-2. The proposed remedies in POD and the advancements of the COTS components, such as chip-scale atomic clocks and better onboard processing units, also present a brighter future for real-time applications that require precise orbits and stable clocks.

Climatology of the Global Summer Monsoon Rainy Seasons: Revisited from a High-Resolution Satellite Climate Data Record

Climatology of the Global Summer Monsoon Rainy Seasons: Revisited from a High-Resolution Satellite Climate Data Record

Pulsed Diode Laser Minibars for Pumping Space-Borne Solid-State Lasers

Laser diode benches (LDBs) with lasing wavelength of 808 nm were developed, manufactured and endurance-tested for utilization as pump lasers for the Nd:YAG oscillator in the climate satellite MERLIN. The LDBs combine customized TE-polarized GaAs-based 5-mm minibars with fast axis collimation lenses to produce a reliable, space qualified component at optical power of 63 W per minibar. Accelerated life tests of 20 LDBs are presented. Except for a small increase in threshold current, no wear out in operating current or sudden failures were observed, and reliable operation over the full mission time of >4 gigashots was confirmed. The electro-optical characteristics remain in the specified ranges after exposure to the total operational load of the mission.

Revisiting the light time correction in gravimetric missions like GRACE and GRACE follow-on

The gravity field maps of the satellite gravimetry missions Gravity Recovery and Climate Experiment (GRACE ) and GRACE Follow-On are derived by means of precise orbit determination. The key observation is the biased inter-satellite range, which is measured primarily by a K-Band Ranging system (KBR) in GRACE and GRACE Follow-On. The GRACE Follow-On satellites are additionally equipped with a Laser Ranging Interferometer (LRI), which provides measurements with lower noise compared to the KBR. The biased range of KBR and LRI needs to be converted for gravity field recovery into an instantaneous range, i.e. the biased Euclidean distance between the satellites’ center-of-mass at the same time. One contributor to the difference between measured and instantaneous range arises due to the nonzero travel time of electro-magnetic waves between the spacecraft. We revisit the calculation of the light time correction (LTC) from first principles considering general relativistic effects and state-of-the-art models of Earth’s potential field. The novel analytical expressions for the LTC of KBR and LRI can circumvent numerical limitations of the classical approach. The dependency of the LTC on geopotential models and on the parameterization is studied, and afterwards the results are compared against the LTC provided in the official datasets of GRACE and GRACE Follow-On. It is shown that the new approach has a significantly lower noise, well below the instrument noise of current instruments, especially relevant for the LRI, and even if used with kinematic orbit products. This allows calculating the LTC accurate enough even for the next generation of gravimetric missions.

Evaluation of hydrological and cryospheric angular momentum estimates based on GRACE, GRACE-FO and SLR data for their contributions to polar motion excitation

In geodesy, a key application of data from the Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GRACE-FO), and Satellite Laser Ranging (SLR) is an interpretation of changes in polar motion excitation due to variations in the Earth’s surficial fluids, especially in the continental water, snow, and ice. Such impacts are usually examined by computing hydrological and cryospheric polar motion excitation (hydrological and cryospheric angular momentum, HAM/CAM). Three types of GRACE and GRACE-FO data can be used to determine HAM/CAM, namely degree-2 order-1 spherical harmonic coefficients of geopotential, gridded terrestrial water storage anomalies computed from spherical harmonic coefficients, and terrestrial water storage anomalies obtained from mascon solutions. This study compares HAM/CAM computed from these three kinds of gravimetric data. A comparison of GRACE-based excitation series with HAM/CAM obtained from SLR is also provided. A validation of different HAM/CAM estimates is conducted here using the so-called geodetic residual time series (GAO), which describes the hydrological and cryospheric signal in the observed polar motion excitation. Our analysis of GRACE mission data indicates that the use of mascon solutions provides higher consistency between HAM/CAM and GAO than the use of other datasets, especially in the seasonal spectral band. These conclusions are confirmed by the results obtained for data from first 2 years of GRACE-FO. Overall, after 2 years from the start of GRACE-FO, the high consistency between HAM/CAM and GAO that was achieved during the best GRACE period has not yet been repeated. However, it should be remembered that with the systematic appearance of subsequent GRACE-FO observations, this quality can be expected to increase. SLR data can be used for determination of HAM/CAM to fill the one-year-long data gap between the end of GRACE and the start of the GRACE-FO mission. In addition, SLR series could be particularly useful in determination of HAM/CAM in the non-seasonal spectral band. Despite its low seasonal amplitudes, SLR-based HAM/CAM provides high phase consistency with GAO for annual and semiannual oscillation.