An Improved Method Combining CNN and 1D-Var for the Retrieval of Atmospheric Humidity Profiles from FY-4A/GIIRS Hyperspectral Data

FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) is the first infrared hyperspectral atmospheric vertical detector in geostationary orbit. Compared to other similar instruments, it has the advantages of high temporal resolution and stationary relative to the ground. Based on the characteristics of GIIRS observation data, we proposed a humidity profile retrieval method. We fully utilized the information provided by the observation and forecast data, and used the two-dimensional brightness temperature data with the dimension of time and optical spectrum as the input of the CNN (convolution neural network model). Then, the obtained brightness temperature data were shown to be more suitable as the input for the physical retrieval method for humidity than the conventional correction method, improving the accuracy of humidity profile retrieval. We performed two comparative experiments. The first experiment results indicate that, compared to ordinary linear correction and ANN (artificial neural network algorithm) correction, our revised observed brightness temperature data are much closer to the simulated brightness temperature obtained by inputting ERA5 reanalysis data into RTTOV (Radiative Transfer for TOVS). The results of the second experiment indicate that the accuracy of the humidity profile retrieved by our method is higher than that of conventional ANN and 1D-Var (one-dimensional variational algorithm). With ERA5 reanalysis data as the reference value, the RMSE (Root Mean Squared Error) of the humidity profiles by our method is less than 8.2% between 250 and 600 hPa. Our method holds the unique advantage of the high temporal resolution of GIIRS, improves the accuracy of humidity profile retrieval, and proves that the combination of machine learning and the physical method is a compelling idea in the field of satellite atmospheric remote sensing worthy of further exploration.

A Study on the Retrieval of Temperature and Humidity Profiles Based on FY-3D/HIRAS Infrared Hyperspectral Data

Satellite infrared hyperspectral instruments can obtain a wealth of atmospheric spectrum information. In order to obtain high-precision atmospheric temperature and humidity profiles, we used the traditional One-Dimensional Variational (1D-Var) retrieval algorithm, combined with the information capacity-weight function coverage method to select the spectrum channel. In addition, an Artificial Neural Network (ANN) algorithm was introduced to correct the satellite observation data error and compare it with the conventional error correction method. Finally, to perform the temperature and humidity profile retrieval calculation, we used the FY-3D satellite HIRAS (Hyperspectral Infrared Atmospheric Sounder) infrared hyperspectral data and combined the RTTOV (Radiative Transfer for TOVS) radiative transfer model to build an atmospheric temperature and humidity profile retrieval system. We used data on the European region from July to August 2020 to carry out the training and testing of the retrieval system, respectively, and used the balloon-retrieved sounding data of temperature and humidity published by the University of Wyoming as standard truth values to evaluate the retrieval accuracy. Our preliminary research results show that, compared with the retrieval results of conventional deviation correction, the introduction of ANN algorithm error correction can improve the retrieval accuracy of the retrieval system effectively and the RMSE (Root-Mean-Square Error) of the temperature and humidity has a maximum accuracy of improvement of about 0.5 K (The K represents the thermodynamic temperature unit) and 5%, respectively. The temperature and humidity results obtained by the retrieval system were compared with Global Forecast System (GFS) forecast data. The retrieved temperature RMSE was less than 1.5 K on average, which was better than that for the GFS; the humidity RMSE was less than 15% as a whole, and better than the forecast profile between 100 hpa (1 hpa is 100 pa, the pa represents the air pressure unit) and 600 hpa. Compared with AIRS (Atmospheric Infrared Sounder) products, the result of the retrieval system also had a higher accuracy. The main improvement of the temperature was at 200 hpa and 800 hpa, with maximum accuracy improvements of 2 K and 1.5 K, respectively. The RMSE of the humidity retrieved by the system was also better than the AIRS humidity products at most pressure levels, and the error of maximum difference could reach 15%. After combining the two algorithms, the FY-3D/HIRAS infrared hyperspectral retrieval system could obtain higher-precision temperature and humidity profiles, and relevant results could provide a reference for improving the accuracy of business products.

Validating a Termite-Inspired Construction Coordination Mechanism Using an Autonomous Robot

Many species of termites build large, structurally complex mounds, and the mechanisms behind this coordinated construction have been a longstanding topic of investigation. Recent work has suggested that humidity may play a key role in the mound expansion of savannah-dwelling Macrotermes species: termites preferentially deposit soil on the mound surface at the boundary of the high-humidity region characteristic of the mound interior, implying a coordination mechanism through environmental feedback where addition of wet soil influences the humidity profile and vice versa. Here we test this potential mechanism physically using a robotic system. Local humidity measurements provide a cue for material deposition. As the analogue of the termite's deposition of wet soil and corresponding local increase in humidity, the robot drips water onto an absorbent substrate as it moves. Results show that the robot extends a semi-enclosed area outward when air is undisturbed, but closes it off when air is disturbed by an external fan, consistent with termite building activity in still vs. windy conditions. This result demonstrates an example of adaptive construction patterns arising from the proposed coordination mechanism, and supports the hypothesis that such a mechanism operates in termites.

Field calibration and correction of air water concentration measurements

<p>Infrared gas analyzers (IRGAs) are commonly used in Eddy Covariance (EC) system and are used for, in particular, the ecosystem water cycle. However, they suffer from a measurement drift of absolute concentrations with time, leading to the increasing bias of readings. It is recommended in the manuals to do a factory calibration once every 1-2 years (e.g., LI-6262) or user calibration when considerable drift occurs (e.g., LI-7000). However, our experience shows that a significant drift can occur within a few days already. At our semi-arid EC site of Yatir Forest (31˚20'N, 35˚03'E, Israel), we are measuring a vertical air humidity profile (absolute humidity, C<sub>w</sub> in mmol×mol<sup>-1</sup>, and relative, RH, %),  to study the VPD regime within the canopy and to analyze dew formation events, which requires highly accurate RH measurements, however accurate RH measurements are difficult to achieve.</p><p>Air humidity in Yatir is measured by three different instruments: (1) LI-7000 close-pass IRGA above the canopy for EC flux calculations; (2) LI-6262 close-pass IRGA with inlets in 4 different heights from above the ground up to the sonic height, used for humidity profile measurements; (3) Rotronic HC2S3 air humidity (RH) and temperature (T) sensor above the canopy. Both IRGAs are placed within a temperature-controlled box, and calibrated for zero and span with N2, dew point generator and laboratory standard gases every 1-2 weeks. The Rotronic sensor has very low drift and does not require calibration, but is assumed to be less accurate, especially under high and low RH.</p><p>To achieve highly accurate measurements on daily time scale we propose a correction routine that rely on the stability of the RH probe, and the accuracy of the IRGAs after calibration. Every time the IRGA is calibrated, a correction-1 to the RH probe is produced. Between calibrations, the trends in the drifting IRGAs data are corrected (correction-2) to the interpolated stable RH probe data.</p><p>For the flux measurements, the mean absolute Cw error before correction was 1.0 mmol×mol<sup>-1</sup>, which translates under average temperature of 25<sup>°</sup>C and RH of 50% to errors of RH, VPD and dew point of 3.0%, 93.5 Pa and 0.9<sup>°</sup>C, respectively. Following our correction procedure, reduced the error to 0.5 mmol×mol<sup>-1</sup>, which decreased the errors in RH, VPD and dew point under the same conditions to 1.5%, 47 Pa and 0.4<sup>°</sup>C, respectively. For the humidity profile, Cw error after correction decreased from 1.9 mmol×mol<sup>-1</sup> to 0.5 mmol×mol<sup>-1</sup>, which decreased the errors in RH, VPD and dew point under the same conditions by 4.1%, 131 Pa and 1.2<sup>°</sup>C, respectively.</p><p>We will describe the method in more detail and demonstrate its application to our field measurements.</p>

Combining Differential Absorption Radar and Microwave Radiometer for Water Vapor Profiling in the Cloudy Trade-Wind Environment

<p>Understanding atmospheric processes, such as e.g. cloud and precipitation formation, requires high-resolution water vapor and temperature profile observations particularly in the cloudy boundary-layer. As current observation techniques are limited by low spatial or temporal resolution, the potential of combining microwave radiometer (MWR) with differential absorption radar is investigated by analysing the retrieval information content and retrieval uncertainty. Two radar frequency combinations are analyzed: Ka- and W-band (KaW), available at e.g. Barbados Cloud Observatory, as well as a synthetic combination of G-band frequencies (167 and 175 GHz, G2), simulated using the Passive and Active Microwave TRAnsfer model PAMTRA.</p><p>The novel synergistic retrieval approach is based on an optimal estimation retrieval scheme. The absolute humidity profile is retrieved from the MWR K-band brightness temperatures, as well as the Dual-Wavelength Ratio (DWR) signal of the two radars. Evaluating a suite of radiosonde profiles measured at Barbados from 2018, adding the active KaW combination to K-band MWR brightness temperatures increases the information content for the retrieved profile from 3.2 to 3.4 degrees of freedom for signal (DoF). The usage of the higher G2 radar frequencies leads to higher Dual-Wavelength Ratios (DWRs), and, in combination with the MWR, to increased DoF (4.5), decreased retrieval errors, and a more realistic retrieved profile within the cloud layer. Information partitioning among MWR and the radars makes the synergy particularly beneficial: the profile below and within the cloud is restricted by the radar observations, whereas the water vapor above cloud top and the LWP are constrained by the MWR.</p><p>Based on selected case studies with single- as well as multi-layered clouds from the EUREC4A campaign, different retrieval configurations will be evaluated based on the resulting retrieval error, as well as the Degrees of Freedom. Tools for customizing the retrieval to the trade wind driven atmosphere will be analyzed by e.g. constraining the humidity profile to saturation within the cloud layer, or making use of a direct inversion approach of the differential attenuation signals.</p>

Space experiment "Convergence": retrieving of atmospheric water vapor profile using of artificial neural networks

The paper is carried out to the investigation of the possibility of retrieving absolute humidity profile of the atmosphere using an artificial neural network based on the modeling of radiometric data of the passive microwave complex MIRS, which is part of the scientific equipment of the space experiment "Convergence". The main approaches to the construction of artificial neural networks are considered. The process of modeling MIRSs radiometric data are described. Selection of optimal characteristics of the neural network is carried out. Necessity of the information about atmospheric temperature profile for the best accuracy in solving the inverse problem are shown. The advantages of using differential channels in the 22 GHz absorption band for the humidity profile retrieving are proved. The expected errors of the atmospheric humidity profile retrieving during the Convergence experiment at altitudes from 0 to 10 km are given.

Re-Examining the First Climate Models: Climate Sensitivity of a Modern Radiative–Convective Equilibrium Model

We revisit clear-sky one-dimensional radiative–convective equilibrium (1D-RCE) and determine its equilibrium climate sensitivity to a CO2 doubling (ECS) and associated uncertainty. Our 1D-RCE model, named konrad, uses the Rapid Radiative Transfer Model for GCMs (RRTMG) to calculate radiative fluxes in the same way as in comprehensive climate models. The simulated radiative feedbacks are verified by a line-by-line radiative transfer model, with which we also investigate their spectral distribution. Changing the model configuration of konrad enables a clear separation between the water vapor and the lapse rate feedbacks, as well as the interaction between the two. We find that the radiative feedback and ECS are sensitive to the chosen relative humidity profile, resulting in an ECS range of 2.09–2.40 K. Using larger CO2 forcings we find that the radiative feedback changes up to 10% for surface temperatures of 291–299 K. Although the ECS is similar to previous studies, it arises from the compensation of a larger clear-sky forcing (4.7 W m−2) and more strongly negative feedbacks (−2.3 W m−2 K−1). The lapse rate feedback and the feedback from the interaction of lapse rate and humidity compensate each other, but the degree of compensation depends on the relative humidity profile. Additionally, the temperature profile is investigated in a warming climate. The temperature change at the convective top is half as large as at the surface, consistent with the proportionally higher anvil temperature hypothesis, as long as the humidity is consistently coupled to the temperature profile.