竹中 栄晶

<title>Abstract</title>We developed lookup tables for the correlated <italic>k</italic>-distribution (CKD) method in the 940 nm water vapor absorption region (WV-CKD), with the aim of rapid and accurate computation of narrow-band radiation around 940 nm (10,000–10,900 <inline-formula><alternatives><tex-math>$${\mathrm{cm } }^{-1}$$</tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>cm</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:math></alternatives></inline-formula>) for ground-based angular-scanning radiometer data analysis. Tables were constructed at three spectral resolutions (2, 5, and 10 <inline-formula><alternatives><tex-math>$${\mathrm{cm } }^{-1}$$</tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>cm</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:math></alternatives></inline-formula>) with quadrature values (point and weight) and numbers optimized using simulated sky radiances at ground level, which had accuracies of ≤ 0.5% for sub-bands of <inline-formula><alternatives><tex-math>$$10 {\mathrm{cm } }^{-1}$$</tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>10</mml:mn> <mml:msup> <mml:mrow> <mml:mi>cm</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math></alternatives></inline-formula>. Although high-resolution WV-CKD requires numerous quadrature points, the number of executions of the radiative transfer model is reduced to approximately 1/46 of the number used in the line-by-line approach by our WV-CKD with a resolution of 2 <inline-formula><alternatives><tex-math>$${\mathrm{cm } }^{-1}$$</tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>cm</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:math></alternatives></inline-formula>. Furthermore, we confirmed through several simulations that WV-CKD could be used to compute radiances with various vertical profiles. The accuracy of convolved direct solar irradiance and diffuse radiance at a full width at half maximum (FWHM) of 10 nm, computed with the WV-CKD, is &lt; 0.3%. In contrast, the accuracy of convolved normalized radiance, which is the ratio of diffuse radiance to direct solar irradiance, at an FWHM of 10 nm computed with the WV-CKD is &lt; 0.11%. This accuracy is lower than the observational uncertainty of a ground-based angular-scanning radiometer (approximately 0.5%). Finally, we applied the SKYMAP and DSRAD algorithms (Momoi et al. in Atmos Meas Tech 13:2635–2658, 2020. <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" xlink:href="https://doi.org/10.5194/amt-13-2635-2020">10.5194/amt-13-2635-2020</ext-link>) to SKYNET observations (Chiba, Japan) and compared the results with microwave radiometer values. The precipitable water vapor (PWV) derived with the WV-CKD showed better agreement (correlation coefficient <italic>γ</italic> = 0.995, slope = 1.002) with observations than PWV derived with the previous CKD table (correlation coefficient <italic>γ</italic> = 0.984, slope = 0.926) by Momoi et al. (Momoi et al., Atmos Meas Tech 13:2635–2658, 2020). Through application of the WV-CKD to actual data analysis, we found that an accurate CKD table is essential for estimating PWV from sky-radiometer observations.

<title>Abstract</title>Assessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10–15 min. Our analysis of NDVI data collected over the Amazon during 2018–19 shows that ABI provides 21–35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

<jats:p>This study describes a high-speed correction method for geolocation information of geostationary satellite data for accurate physical analysis. Geostationary satellite observations with high temporal resolution provide instantaneous analysis and prompt reports. We have previously reported the quasi real-time analysis of solar radiation at the surface and top of the atmosphere using geostationary satellite data. Estimating atmospheric parameters and surface albedo requires accurate geolocation information to estimate the solar radiation accurately. The physical analysis algorithm for Earth observations is verified by the ground truth. In particular, downward solar radiation at the surface is validated by pyranometers installed at ground observation sites. The ground truth requires that the satellite observation data pixels be accurately linked to the location of the observation equipment on the ground. Thus, inaccurate geolocation information disrupts verification and causes complex problems. It is difficult to determine whether error in the validation of physical quantities arises from the estimation algorithm, satellite sensor calibration, or a geolocation problem. Geolocation error hinders the development of accurate analysis algorithms; therefore, accurate observational information with geolocation information based on latitude and longitude is crucial in atmosphere and land target analysis. This method provides the basic data underlying physical analysis, parallax correction, etc. Because the processing speed is important in geolocation correction, we used the phase-only correlation (POC) method, which is fast and maintains the accuracy of geolocation information in geostationary satellite observation data. Furthermore, two-dimensional fast Fourier transform allowed the accurate correction of multiple target points, which improved the overall accuracy. The reference dataset was created using NASA’s Shuttle Radar Topography Mission 1-s mesh data. We used HIMAWARI-8/Advanced HIMAWARI Imager data to demonstrate our method, with 22,709 target points for every 10-min observation and 5826 points for every 2.5 min observation. Despite the presence of disturbances, the POC method maintained its accuracy. Column offset and line offset statistics showed stability and characteristic error trends in the raw HIMAWARI standard data. Our method was sufficiently fast to apply to quasi real-time analysis of solar radiation every 10 and 2.5 min. Although HIMAWARI-8 is used as an example here, our method is applicable to all geostationary satellites. The corrected HIMAWARI 16 channel gridded dataset is available from the open database of the Center for Environmental Remote Sensing (CEReS), Chiba University, Japan. The total download count was 50,352,443 on 8 July 2020. Our method has already been applied to NASA GeoNEX geostationary satellite products.</jats:p>

<jats:p>Recent advancements in new generation geostationary satellites have facilitated the application of their datasets to terrestrial monitoring. In this application, geolocation accuracy is an essential issue because land surfaces are generally heterogeneous. In the case of the Advanced Himawari Imager (AHI) onboard Himawari-8, geometric correction of the Himawari Standard Data provided by the Japan Meteorological Agency (JMA data) was conducted using thermal infrared band with 2 km spatial resolution. Based on JMA data, the Center for Environmental Remote Sensing (CEReS) at Chiba University applied a further geometric correction using a visible band with 500 m spatial resolution and released a dataset (CEReS data). JMA data target more general users mainly for meteorological observations, whereas CEReS data aim at terrestrial monitoring for more precise geolocation accuracy. The objectives of this study are to clarify the temporal and spatial variations of geolocation errors in these two datasets and assess their stability for unexpected large misalignment. In this study, the temporal tendencies of the relative geolocation difference between the two datasets were analyzed, and temporal fluctuations of band 3 reflectances of JMA data and CEReS data at certain fixed sites were investigated. A change in the geolocation trend and occasional shifts greater than 2 pixels were found in JMA data. With improved image navigation performance, the geolocation difference was decreased in CEReS data, suggesting the high temporal stability of CEReS data. Overall, JMA data showed an accuracy of less than 2 pixels with the spatial resolution of band 3. When large geolocation differences were observed, anomalies were also detected in the reflectance of JMA data. Nevertheless, CEReS data successfully corrected the anomalous errors and achieved higher geolocation accuracy in general. As CEReS data are processed during the daytime due to the availability of visible bands, we suggest the use of CEReS data for effective terrestrial monitoring during the daytime.</jats:p>

<jats:p>GeoNEX is a collaborative project led by scientists from NASA, NOAA, and many other institutes around the world to generate Earth monitoring products using data streams from the latest Geostationary (GEO) sensors including the GOES-16/17 Advanced Baseline Imager (ABI), the Himawari-8/9 Advanced Himawari Imager (AHI), and more. An accurate and consistent product of the Top-Of-Atmosphere (TOA) reflectance and brightness temperature is the starting point in the scientific processing pipeline and has significant influences on the downstream products. This paper describes the main steps and the algorithms in generating the GeoNEX TOA products, starting from the conversion of digital numbers to physical quantities with the latest radiometric calibration information. We implement algorithms to detect and remove residual georegistration uncertainties automatically in both GOES and Himawari L1bdata, adjust the data for topographic relief, estimate the pixelwise data-acquisition time, and accurately calculate the solar illumination angles for each pixel in the domain at every time step. Finally, we reproject the TOA products to a globally tiled common grid in geographic coordinates in order to facilitate intercomparisons and/or synergies between the GeoNEX products and existing Earth observation datasets from polar-orbiting satellites.</jats:p>

In this paper, we propose a day-ahead strategic marketing method for multi-period energy markets using a machine learning approach based on neural networks. An aggregator, which has renewable energy generation devices, needs to schedule the energy production and consumption (prosumption) in a situation where the renewable power generation amount is not exactly predicted in day-ahead scheduling. If imbalance, defined as the difference between a day-ahead schedule and an actual prosumption profile, occurs, the aggregator is required to pay imbalance penalty costs. As a scheduling method to avoid paying imbalance penalty costs, we propose a scheduling model by machine learning based on the results of past transactions. In particular, the scheduling model is given as a neural network, which has an advantage in terms of computational costs compared to the kernel method. For developing a training algorithm, we show that the gradient of the profit function with respect to design parameters can be calculated as a solution to linear programming. Finally, we show the efficiency of the proposed method through a numerical example.

In this paper, we propose a day-ahead scheduling method under uncertain renewable energy generation based on a machine learning approach. An aggregator, which has renewable energy generation devices, needs to schedule the energy production and consumption (prosumption) in a situation where the renewable power generation amount is not exactly predicted at day-ahead scheduling. If imbalance, defined as the difference between a day-ahead schedule and a prosumption profile on the next day in the day-ahead energy market, occurs, the aggregator must pay imbalance adjustment costs. As a scheduling method to avoid paying imbalance adjustment costs, we propose a scheduling model by machine learning based on the results of past transactions. We first formulate a problem of constructing a scheduling model as a problem of finding parameters involved in the scheduling model. Next, by introducing a kernel method, we show that the problem of finding the parameter maximizing the mean of profits of past transactions is a concave program. Furthermore, by introducing piecewise affine cost functions, we also show that the problem of finding the parameter can be formulated as a quadratic program. Finally, we show the efficiency of the proposed method through a numerical example.

A data interface system was constructed to enable the effective use of quasi-real-time solar radiation and solar power generation estimates based on data from the Himawari satellite in the energy management system. As one of the efforts introduced to apply meteorological data to the management of energy systems with renewable energy, we provided weather information to our solar car race team. We constructed a data interface system that provided data via web forms on the Azure cloud and on-premises servers. The implementation of a JSON format WebAPI enabled seamless data provision.

<jats:p>To realize the safety control of electric power systems under high penetration of photovoltaic power systems, accurate global horizontal irradiance (GHI) forecasts using numerical weather prediction models (NWP) are becoming increasingly important. The objective of this study is to understand meteorological characteristics pertaining to large errors (i.e., outlier events) of GHI day-ahead forecasts obtained from the Japan Meteorological Agency, for nine electric power areas during four years from 2014 to 2017. Under outlier events in GHI day-ahead forecasts, several sea-level pressure (SLP) patterns were found in 80 events during the four years; (a) a western edge of anticyclone over the Pacific Ocean (frequency per 80 outlier events; 48.8%), (b) stationary fronts (20.0%), (c) a synoptic-scale cyclone (18.8%), and (d) typhoons (tropical cyclones) (8.8%) around the Japanese islands. In this study, the four case studies of the worst outlier events were performed. A remarkable SLP pattern was the case of the western edge of anticyclone over the Pacific Ocean around Japan. The comparison between regionally integrated GHI day-ahead forecast errors and cloudiness forecasts suggests that the issue of accuracy of cloud forecasts in high- and mid-levels troposphere in NWPs will remain in the future.</jats:p>

Abstract. Observations from the new Japanese geostationary satellite Himawari-8 permit quasi-real-time estimation of global shortwave radiation at an unprecedented temporal resolution. However, accurate comparisons with ground-truthing observations are essential to assess their uncertainty. In this study, we evaluated the Himawari-8 global radiation product AMATERASS using observations recorded at four SKYNET stations in Japan and, for certain analyses, from the surface network of the Japanese Meteorological Agency in 2016. We found that the spatiotemporal variability of the satellite estimates was smaller than that of the ground observations; variability decreased with increases in the time step and spatial domain. Cloud variability was the main source of uncertainty in the satellite radiation estimates, followed by direct effects caused by aerosols and bright albedo. Under all-sky conditions, good agreement was found between satellite and ground-based data, with a mean bias in the range of 20–30 W m−2 (i.e., AMATERASS overestimated ground observations) and a root mean square error (RMSE) of approximately 70–80 W m−2. However, results depended on the time step used in the validation exercise, on the spatial domain, and on the different climatological regions. In particular, the validation performed at 2.5 min showed largest deviations and RMSE values ranging from about 110 W m−2 for the mainland to a maximum of 150 W m−2 in the subtropical region. We also detected a limited overestimation in the number of clear-sky episodes, particularly at the pixel level. Overall, satellite-based estimates were higher under overcast conditions, whereas frequent episodes of cloud-induced enhanced surface radiation (i.e., measured radiation was greater than expected clear-sky radiation) tended to reduce this difference. Finally, the total mean bias was approximately 10–15 W m−2 under clear-sky conditions, mainly because of overall instantaneous direct aerosol forcing efficiency in the range of 120–150 W m−2 per unit of aerosol optical depth (AOD). A seasonal anticorrelation between AOD and global radiation differences was evident at all stations and was also observed within the diurnal cycle.

A case study of rapid variation (ramp event) of photovoltaic (PV) power generation on 18 April 2016 for the Tokyo Electric Power Company (called "TEPCO") power area is considered in this study. PV system installation in the TEPCO area has been increasing (approximately 10 GW PV system capacity). Quasi real-time monitoring of PV power generation by using satellite-estimated solar radiation is performed. Under assumptions of future PV system installation scenarios (from 21.78 GW to 85.76 GW in the TEPCO area), temporal and spatial variations of PV power generation are estimated. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

This paper presents a distribution automation system (DAS) for service restoration in the distribution network with photovoltaic (PV) generator systems, which are disconnected simultaneously after a fault and subsequently reconnected after service restoration. Because the reverse power flow of PVs affects voltage in the distribution system, voltage dips and surges occur during the service restoration. However, current DAS do not control voltage regulators such as an on-load tap changer (OLTC) and step voltage regulators (SVRs) during the service restoration. The proposed DAS estimates the voltage in a distribution network considering the simultaneous disconnection of PVs by performing power flow calculations, and it controls the tap position of OLTC and/or SVRs according to the predicted voltage deviation. The voltage after the disconnection of PVs is calculated by estimating the PV output utilizing square kilometer solar radiation data calculated using satellite image data in real time.

全地球規模の大気観測の主力センサーとして,衛星搭載型可視赤外イメージャーが活発に利用されている. 極軌道衛星に搭載されたMODISセンサーやGLIセンサーのように広い観測幅を有するイメージャーは, ほぼ毎日の全球規模観測が可能であることから, 気象現象から気候問題までの様々な時空間スケールの観測に用いられている. 静止気象衛星搭載イメージャーを用いれば更に高頻度な観測が可能となる. 搭載する多波長バンドを利用して多くの大気物理パラメータを定量的に観測ができるのもイメージャーの特徴である. 本論文では, 極軌道衛星および静止軌道衛星搭載イメージャーのデータ解析から見えてくる諸現象のうち, 気候の形成にかかわる雲, 放射, エアロゾル間接効果に注目しながら, 可視赤外イメージャーの有用性について論じる.Polar orbital multi-spectral visible-infrared imager, such as the MODIS and the GLI are used for observing the Earth surface from space for several temporal-spatial scales regarding weather and climate, since it covers the earth surface almost everyday. Moreover, unitization of imager aboard the geostationary satellites (e.g. Himawari, GOES, Meteosat) allows observing the Earth more frequently than polar orbital satellite sensors. One of advantages of such imagers is that they can estimate many atmospheric parameters using multi-spectral information. This paper discusses worthiness and usability of the visible-infrared imagers, focusing estimation of cloud, radiation, and indirect effect of aerosols.

This paper investigates cooperative energy network formation for distributed autonomous microgrids based on receding horizon control and game theoretic cooperative control. In particular, we focus on photovoltaics and aim at minimizing its temporal and spatial variability while reducing transmission losses over the whole network by forming an appropriate network of power transmissions. We first formulate a novel optimal network formation problem in the form of resource allocation games so that the welfare function reflects the above objectives. Then, the problem is reduced to a potential game through an existing utility design technique. The paper next presents a variation of a learning algorithm presented in one of the authors' previous works and newly provide a proof of convergence in probability to potential function maximizers. Moreover, we consider real time implementation of the presented framework based on receding horizon control, where it is shown that the information processing of the learning algorithm is almost distributed with the helps of a solar radiation forecasting/estimation system. Finally, this paper illustrates the effectiveness of the present approach through simulation using real data of a solar radiation estimation system.

Aerosol retrieval algorithm of EarthCARE/MSI is divided into two parts. One is ocean algorithm, and the other is land algorithm. To retrieval over ocean, two-channel method of band1 and band2 is used. To retrieve aerosol over land, we need to estimate ground reflectance. One idea to estimate the ground reflectance is to make MSI's original albedo product by choosing minimum reflectance data of MSI. But, the swath of MSI is as narrow as 150km. It is difficult to gather enough radiance data to make ground reflectance. Another idea is to extrapolate ground reflectance on larger wavelength to the ground reflectance on visible wavelength. We did example analysis with several methods and coefficients. Minimum reflectance method of 0.38 um and that of 0.68 um shows close results. On the other hand, Modified Kaufman method shows larger aerosol optical thickness pattern than others.

The direct solar radiation observed by instruments aboard the ground-based solar tracking devices, is needed to estimate aerosol optical thickness. However, the instruments on unstable platforms such as ship sometimes don't accurately point the sun. In our research, the PAR radiometer that can observe direct solar radiation with high stability has been developed. The shadow band of the PAR radiometer assists to obtain stable observation data on any unstable platform. Actually the newer-developed PAR radiometer doesn't need tracking the sun anymore. We have also developed an algorithm that retrieves aerosol optical thickness from the PAR radiometer data. From the experiments performed from 2006 to 2008, aerosol optical thicknesses were estimated with high accuracy.

It is possible to retrieve vertical profiles of cloud microphysical properties, using radar reflectivity factor observed by CPR (Cloud Profiling Radar), which leads to better understanding of cloud radiative forcing. In this study, vertical profiles of cloud microphysical properties (e.g., effective radius, liquid water content, cloud optical thickness, etc,) retrieved from the ground-based FMCW CPR FALCON-I are estimated to evaluate sensitivity and accuracy of FALCON-I at the Hedo observation in 2008. © 2010 IEICE Institute of Electronics Informati.

To assess environmental change at global scale, accurate estimates of surface radiative fluxes at high temporal resolution are needed. An algorithm for the estimation of the shortwave radiation budget from the GMS-5/SVISSR data has been developed. In this study, a component of this algorithm used for deriving COT is evaluated. The COT retrieved from the GMS-5/SVISSR is compared with similar parameters derived from Terra/MODIS during APEX-E2. It was found that the assumption on the effective radius of clouds as well as the sensor quantization noise can introduce a large error in COT derived from GMS-5/SVISSR. In the present analysis we show that the errors in COT of area-level clouds in the aggregate due to unknown effective radius can be reduced progressively as compared to errors of pixel-level ones.

Using ADEOS-II/GLI aerosol and cloud products, downward and upward solar radiation at the surface and at the top of the atmosphere are estimated to study the Earth radiation budget. There is a good agreement in the main features of the global distribution of radiative fluxes as derived from GLI and from Terra/MODIS, yet, some differences can be noticed and need to be explained. In order to evaluate satellite-retrieved parameters that play a role in the Earth radiation budget, an observational network known as SKYNET has been established in Eastern Asia and it has already been operational during the ADEOS-II/GLI launch. Specifically, observations from the newly developed i-sky radiometer have been used for aerosol and cloud product evaluation. The aerosol products have been found to be in good agreement with observations while the cloud products need further evaluation.

The ABC (Atmospheric Brown Cloud project) Gosan campaign 2005 (EAREX2005) was carried out at Gosan on Cheju Island, Korea, in March 2005. The objective of the campaign was to clarify aerosol characteristics as well as to compare each instrument for radiation and chemical observation. From these observations, eleven clear sky cases were selected and analyzed to estimate the aerosol radiative effect (ARE). As a result, the mean ARE during the campaign was -20.8 ± 9.0 W/m2 at the surface, -8.3 ± 5.3 W/m2 at the top of the atmosphere and 12.6 ± 6.8 W/m2 in the atmosphere. The ARE efficiency was -81.6 W/m2, -32.5 W/m2 and 49.4 W/m2, respectively. These results suggest that the aerosols during the campaign might consist of more or less yellow sand in comparison with the results simulated using typical aerosol models. On the basis of simultaneous observation of the depolarization ratio by lidar, a common feature of yellow sand is also found in a daily trend of aerosols through the period. A yellow sand index (YSI) is introduced using a column integration of extinction coefficients for spherical and nonspherical particles, separated empirically by the depolarization ratio. This index is equivalent to the fraction of yellow sand (nonspherical) aerosol in the observed aerosol optical thickness. The YSI has a good correlation with the Angstrom index (α) obtained by sky radiometer observations and shows that the increase in YSI corresponds to the decrease in α and the increase in single scattering albedo of aerosol. However, the YSI is poorly correlated with the ARE efficiency.

Geostationary satellites are well suited for radiation budget computations due to their high temporal resolution. In order to validate satellite observations and the radiative properties derived from the GMS-5/SVISSR, we compared its cloud optical depth (COD) with that from the polar orbiting satellite, TERRA/MODIS. It appears that there's a good agreement between both COD sets in thin cloud areas while, major differences (MODIS COD higher) occur in thick cloud regions. Factors affecting accurate observations of clouds by satellites range from the solar and satellites geometries to the sun-cloud scale of interaction. This study focuses on the latter effect, as the solar and satellite zenith angles are relatively low in the area and time selected. The sun-cloud interactions refer here to the three-dimensional radiative effects (e.g. asymmetry, smoothing) due to the horizontal spatial variability of clouds and their structural inhomogeneity. These are analyzed through the IR thermal gradient and local areas' standard deviation (STDEV) respectively. By combining these two parameters, it is possible to reasonably explain the differences in cloud physical and optical properties noticed between both satellites. Results show that, asymmetry and smoothing effects seem to be stronger for SVISSR data than MODIS. At the sides of the clouds SVISSR observed cloud properties are more or less comparable to MODIS data. At the top of the clouds, SVISSR data are systematically lower and do not match MODIS data. SVISSR observations fail to detect cloud inhomogeneity mostly at the top of the clouds, and therefore seem to underestimate the cloud optical properties.