scholarly journals Development of on-site self-calibration and retrieval methods for sky-radiometer observations of precipitable water vapor

2020 ◽  
Vol 13 (5) ◽  
pp. 2635-2658 ◽  
Author(s):  
Masahiro Momoi ◽  
Rei Kudo ◽  
Kazuma Aoki ◽  
Tatsuhiro Mori ◽  
Kazuhiko Miura ◽  
...  

Abstract. The Prede sky radiometer measures direct solar irradiance and the angular distribution of diffuse radiances at the ultraviolet, visible, and near-infrared wavelengths. These data are utilized for the remote sensing of aerosols, water vapor, ozone, and clouds, but the calibration constant, which is the sensor output current of the extraterrestrial solar irradiance at the mean distance between Earth and the Sun, is needed. The aerosol channels, which are the weak gas absorption wavelengths of 340, 380, 400, 500, 675, 870, and 1020 nm, can be calibrated by an on-site self-calibration method, the Improved Langley method. This on-site self-calibration method is useful for the continuous long-term observation of aerosol properties. However, the continuous long-term observation of precipitable water vapor (PWV) by the sky radiometer remains challenging because calibrating the water vapor absorption channel of 940 nm generally relies on the standard Langley (SL) method at limited observation sites (e.g., the Mauna Loa Observatory) and the transfer of the calibration constant by a side-by-side comparison with the reference sky radiometer calibrated by the SL method. In this study, we developed the SKYMAP algorithm, a new on-site method of self-calibrating the water vapor channel of the sky radiometer using diffuse radiances normalized by direct solar irradiance (normalized radiances). Because the sky radiometer measures direct solar irradiance and diffuse radiance using the same sensor, the normalization cancels the calibration constant included in the measurements. The SKYMAP algorithm consists of three steps. First, aerosol optical and microphysical properties are retrieved using direct solar irradiances and normalized radiances at aerosol channels. The aerosol optical properties at the water vapor channel are interpolated from those at aerosol channels. Second, PWV is retrieved using the angular distribution of the normalized radiances at the water vapor channel. Third, the calibration constant at the water vapor channel is estimated from the transmittance of PWV and aerosol optical properties. Intensive sensitivity tests of the SKYMAP algorithm using simulated data of the sky radiometer showed that the calibration constant is retrieved reasonably well for PWV<2 cm, which indicates that the SKYMAP algorithm can calibrate the water vapor channel on-site in dry conditions. Next, the SKYMAP algorithm was applied to actual measurements under the clear-sky and low-PWV (<2 cm) conditions at two sites, Tsukuba and Chiba, Japan, and the annual mean calibration constants at the two sites were determined. The SKYMAP-derived calibration constants were 10.1 % and 3.2 % lower, respectively, than those determined by a side-by-side comparison with the reference sky radiometer. After determining the calibration constant, we obtained PWV from the direct solar irradiances in both the dry and wet seasons. The retrieved PWV values corresponded well to those derived from a global-navigation-satellite-system–global-positioning-system receiver, a microwave radiometer, and an AERONET (Aerosol Robotic Network) sun–sky radiometer at both sites. The correlation coefficients were greater than 0.96. We calculated the bias errors and the root mean square errors by comparing PWV between the DSRAD (direct solar irradiance) algorithm and other instruments. The magnitude of the bias error and the root mean square error were <0.163 and <0.251 cm for PWV<3 cm, respectively. However, our method tended to underestimate PWV in the wet conditions, and the magnitude of the bias error and the root mean square error became large, <0.594 and <0.722 cm for PWV>3 cm, respectively. This problem was mainly due to the overestimation of the aerosol optical thickness before the retrieval of PWV. These results show that the SKYMAP algorithm enables us to observe PWV over the long term, based on its unique on-site self-calibration method.

2019 ◽  
Author(s):  
Masahiro Momoi ◽  
Rei Kudo ◽  
Kazuma Aoki ◽  
Tatsuhiro Mori ◽  
Kazuhiko Miura ◽  
...  

Abstract. The Prede sky-radiometer, whose aerosol channels are calibrated by on-site measurements (the Improved Langley method), has been used for continuous long-term observation of aerosol properties. However, continuous long-term observation of precipitable water vapor (PWV) by sky-radiometer remain challenge, because the water vapor channel is generally calibrated by the standard Langley method at limited observation sites (e.g., the Mauna Loa Observatory). In this study, we developed SKYMAP, a new onsite self-calibration method for the water vapor channel of the Prede sky-radiometer using diffuse radiances normalized by direct solar irradiance. The SKYMAP algorithm consists of three steps. First, aerosol optical and microphysical properties are retrieved using direct solar irradiances and the normalized diffuse radiances at aerosol channels. The aerosol optical properties at the water vapor channel are interpolated from those at aerosol channels. Second, the transmittance of PWV is retrieved using the diffuse radiance normalized to the direct solar irradiance at the water vapor channel, which does not need the calibration constant. Third, the calibration constant at the water vapor channel is estimated from the transmittance of PWV and aerosol optical properties. Intensive sensitivity tests of SKYMAP using simulated data of the sky-radiometer showed that the calibration constant is retrieved reasonably well for PWV < 2 cm, indicating that SKYMAP can calibrate the water vapor channel on-site in dry conditions. Then SKYMAP was applied to actual measurements in the dry season at two sites (Tsukuba and Chiba, Japan). Because the SKYMAP algorithm is useful for clear-sky and low PWV (< 2 cm) conditions, the water vapor channel was calibrated for the dry season. After determining the calibration constant, PWV is able to be retrieved using direct solar irradiances for the whole year. The retrieved PWV values correspond well to those derived from a Global Navigation Satellite System (GNSS)/Global Positioning System (GPS) receiver, a microwave radiometer, and a AERONET sun-sky radiometer at both sites (correlation coefficient γ > 0.96), indicating that the Prede sky-radiometer provides both aerosol and PWV data based on its unique on-site calibration methods.


2011 ◽  
Vol 50 (12) ◽  
pp. 2460-2472 ◽  
Author(s):  
José A. Ruiz-Arias ◽  
David Pozo-Vázquez ◽  
Vicente Lara-Fanego ◽  
Francisco J. Santos-Alamillos ◽  
J. Tovar-Pescador

AbstractRugged terrain is a source of variability in the incoming solar radiation field, but the influence of terrain is still not properly included by most current numerical weather prediction (NWP) models. In this work, a downscaling postprocessing method for NWP-model solar irradiance through terrain effects is presented. It allows one to decrease the estimation bias caused by terrain shading and sky-view reduction, and to account for elevation variability, surface orientation, and surface albedo. The method has been applied to a case study in southern Spain using the Weather Research and Forecasting (WRF) mesoscale model with a spatial resolution of 30 arc s, resulting in disaggregated maps of 3 arc s. The validation was based on a radiometric network made of eight stations located in the Natural Park of Sierra Mágina over an area of roughly 30 × 35 km2 and 12 carefully selected cloudless days during a year. Three of the stations were equipped with tilted pyranometers. Their inclination and aspect were visually adjusted to the inclination and aspect of the local terrain and then carefully measured. For horizontal surface, the downscaled irradiance has proven to reduce the root-mean-square error of the WRF model by 20% to about 25 W m−2 in winter and autumn and 60 W m−2 in spring and summer. For tilted surface, downscaling to different spatial resolutions resulted in the best performance for 9 arc s, with root-mean-square error of 45% (57 W m−2) and a mean bias error close to zero.


2018 ◽  
Vol 12 (1) ◽  
pp. 352-365 ◽  
Author(s):  
Karn Chalermwongphan ◽  
Prapatpong Upala

Aim: This research aimed to present the process of estimating bicycle traffic demand in order to design bike routes that meet the daily transportation needs of the people in Nakhon Sawan Municipality. Methods: The primary and secondary traffic data were collected to develop a virtual traffic simulation model with the use of the AIMSUN simulation software. The model validation method was carried out to adjust the origin and destination survey data (O/D matrix) by running dynamic O/D adjustment. The 99 replication scenarios were statistically examined and assessed using the goodness-of-fit test. The 9 measures, which were examined, included: 1) Root Mean Square Error (RMSE), 2) Root Mean Square Percentage Error (RMSPE%), 3) Mean Absolute Deviation (MAD), 4) Mean Bias Error (MBE), 5) Mean Percentage Error (MPE%), 6) Mean Absolute Percentage Error (MAPE%), 7) Coefficient of Determination (R2), 8) GEH Statistic (GEH), and 9) Thiel’s U Statistic (Theil’s U). Results: The resulting statistical values were used to determine the acceptable ranges according to the acceptable indicators of each factor. Conclusion: It was found that there were only 8 scenarios that met the evaluation criteria. The selection and ranking process was consequently carried out using the multi-factor scoring method, which could eliminate errors that might arise from applying only one goodness-of-fit test measure.


2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiaoqiao Huang ◽  
Chao Zhang ◽  
Qiong Li ◽  
Yonghang Tai ◽  
Bixuan Gao ◽  
...  

The intermittence and fluctuation character of solar irradiance places severe limitations on most of its applications. The precise forecast of solar irradiance is the critical factor in predicting the output power of a photovoltaic power generation system. In the present study, Model I-A and Model II-B based on traditional long short-term memory (LSTM) are discussed, and the effects of different parameters are investigated; meanwhile, Model II-AC, Model II-AD, Model II-BC, and Model II-BD based on a novel LSTM-MLP structure with two-branch input are proposed for hour-ahead solar irradiance prediction. Different lagging time parameters and different main input and auxiliary input parameters have been discussed and analyzed. The proposed method is verified on real data over 5 years. The experimental results demonstrate that Model II-BD shows the best performance because it considers the weather information of the next moment, the root mean square error (RMSE) is 62.1618 W/m2, the normalized root mean square error (nRMSE) is 32.2702%, and the forecast skill (FS) is 0.4477. The proposed algorithm is 19.19% more accurate than the backpropagation neural network (BPNN) in terms of RMSE.


2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ahmad Fudholi ◽  
Mohd Yusof Othman ◽  
Mohd Hafidz Ruslan ◽  
Kamaruzzaman Sopian

This study evaluated the performance of solar drying in the Malaysian red chili (Capsicum annuumL.). Red chilies were dried down from approximately 80% (wb) to 10% (wb) moisture content within 33 h. The drying process was conducted during the day, and it was compared with 65 h of open sun drying. Solar drying yielded a 49% saving in drying time compared with open sun drying. At the average solar radiation of 420 W/m2and air flow rate of 0.07 kg/s, the collector, drying system, and pickup demonstrated efficiency rates of approximately 28%, 13%, and 45%, respectively. Evaporative capacity ranged from 0.13 to 2.36 kg/h, with an average of 0.97 kg/h. The specific moisture extraction rate (SMER) of 0.19 kg/kWh was obtained. Moreover, the drying kinetics ofC. annuumL. were investigated. A nonlinear regression procedure was used to fit three drying models. These models were compared with experimental data on red chilies dried by open sun drying and those dried by solar drying. The fit quality of the models was evaluated using their coefficient of determination (R2), mean bias error, and root-mean-square error values. The Page model resulted in the highestR2and the lowest mean bias and root-mean-square errors.


1994 ◽  
Vol 110 (1) ◽  
pp. 75-83 ◽  
Author(s):  
Charles Speaks ◽  
Timothy D. Trine ◽  
Thomas R. Crain ◽  
Nancy Niccum

Two experiments were conducted to examine the intelligibility of 72 passages of connected discourse prepared by Cox and McDaniel1,2 in their development of the Speech Intelligibility Rating (SIR) test. Intelligibility was assessed with a method-of-adjustment (MOA) procedure in which listeners adjusted the level of a multi-talker babble until they could just understand 50% of a passage; the measure of intelligibility was the signal-to-babble ratio, dB S/B. The objective was to develop a Revised Speech Intelligibility Rating (RSIR) test that would comprise a large number of equivalent passages that produce reliable intelligibility measures. In experiment 1, the S/B ratio was based on the overall root-mean-square (rms) levels of speech and babble, as represented by the average level of frequent peaks observed on a VU meter. Across all 72 passages, mean intelligibility was −1.43 dB S/B, and the measure of intelligibility for 42 passages was within ±0.5 dB of the overall mean for all 72 passages. In experiment 2, the S/B ratio was based on long-term rms levels of speech and babble measured in 16 one-third-octave bands, with center frequencies from 160 to 5000 Hz. In an effort to achieve greater equivalence in intelligibility among passages, the overall rms level of each passage was attenuated by the difference between SB16-band for an individual passage and S/B16-band for a reference passage. Mean intelligibility across all 72 passages was — 8.06 dB, and the measure of intelligibility was within ±0.5 dB of the overall mean for 64 of the 72 passages. For those 64 passages, the 95% critical difference for five MOAs was 0.72 dB, which corresponds to an estimated percentage critical difference Of 10.8%.


2020 ◽  
Vol 12 (13) ◽  
pp. 2149
Author(s):  
Chang Ki Kim ◽  
Hyun-Goo Kim ◽  
Yong-Heack Kang ◽  
Chang-Yeol Yun ◽  
Yun Gon Lee

Solar irradiance derived from satellite imagery is useful for solar resource assessment, as well as climate change research without spatial limitation. The University of Arizona Solar Irradiance Based on Satellite–Korea Institute of Energy Research (UASIBS-KIER) model has been updated to version 2.0 in order to employ the satellite imagery produced by the new satellite platform, GK-2A, launched on 5 December 2018. The satellite-derived solar irradiance from UASIBS-KIER model version 2.0 is evaluated against the two ground observations in Korea at instantaneous, hourly, and daily time scales in comparison with the previous version of UASIBS-KIER model that was optimized for the COMS satellite. The root mean square error of the UASIBS-KIER model version 2.0, normalized for clear-sky solar irradiance, ranges from 4.8% to 5.3% at the instantaneous timescale when the sky is clear. For cloudy skies, the relative root mean square error values are 14.5% and 15.9% at the stations located in Korea and Japan, respectively. The model performance was improved when the UASIBS-KIER model version 2.0 was used for the derivation of solar irradiance due to the finer spatial resolution. The daily aggregates from the proposed model are proven to be the most reliable estimates, with 0.5 km resolution, compared with the solar irradiance derived by the other models. Therefore, the solar resource map built by major outputs from the UASIBS-KIER model is appropriate for solar resource assessment.


2014 ◽  
Vol 14 (18) ◽  
pp. 9583-9596 ◽  
Author(s):  
P. Chazette ◽  
F. Marnas ◽  
J. Totems ◽  
X. Shang

Abstract. The Infrared Atmospheric Sounding Interferometer (IASI) is a new generation spaceborne passive sensor mainly dedicated to meteorological applications. Operational Level-2 products have been available via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) for several years. In particular, vertical profiles of water vapor measurements are retrieved from infrared radiances at the global scale. Nevertheless, the robustness of such products has to be checked because only a few validations have been reported. For this purpose, the field experiments that were held during the HyMeX and ChArMEx international programs are a very good opportunity. A H2O-Raman lidar was deployed on the Balearic island of Menorca and operated continuously for ~ 6 and ~ 3 weeks during fall 2012 (Hydrological cycle in the Mediterranean eXperiment – HyMeX) and summer 2013 (Chemistry–Aerosol Mediterranean Experiment – ChArMEx), respectively. It measured simultaneously the water vapor mixing ratio and aerosol optical properties. This article does not aim to describe the IASI operational H2O inversion algorithm, but to compare the vertical profiles derived from IASI onboard (meteorological operational) MetOp-A and the ground-based lidar measurements to assess the reliability of the IASI operational product for the water vapor retrieval in both the lower and middle troposphere. The links between water vapor contents and both the aerosol vertical profiles and the air mass origins are also studied. About 30 simultaneous observations, performed during nighttime in cloud free conditions, have been considered. For altitudes ranging from 2 to 7 km, root mean square errors (correlation) of ~ 0.5 g kg−1 (~ 0.77) and ~ 1.1 g kg−1 (~ 0.72) are derived between the operational IASI product and the available lidar profiles during HyMeX and ChArMEx, respectively. The values of both root mean square error and correlation are meaningful and show that the operational Level-2 product of the IASI-derived vertical water vapor mixing ratio can be considered for meteorological and climatic applications, at least in the framework of field campaigns.


1991 ◽  
Vol 96 (D4) ◽  
pp. 7531 ◽  
Author(s):  
J. R. Herman ◽  
R. Hudson ◽  
R. McPeters ◽  
R. Stolarski ◽  
Z. Ahmad ◽  
...  

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