scholarly journals Minimum Local Emissivity Variance Retrieval of Cloud Altitude and Effective Spectral Emissivity—Simulation and Initial Verification

2004 ◽  
Vol 43 (5) ◽  
pp. 795-809 ◽  
Author(s):  
Hung-Lung Huang ◽  
William L. Smith ◽  
Jun Li ◽  
Paolo Antonelli ◽  
Xiangqian Wu ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Spectral Resolution ◽  
Single Layer ◽  
Spectral Emissivity ◽  
Pressure Level ◽  
High Spectral Resolution ◽  
Mean Square ◽  
Atmospheric Conditions ◽  
Wide Range ◽  
Mean Square Errors

Abstract This paper describes the theory and application of the minimum local emissivity variance (MLEV) technique for simultaneous retrieval of cloud pressure level and effective spectral emissivity from high-spectral-resolution radiances, for the case of single-layer clouds. This technique, which has become feasible only with the recent development of high-spectral-resolution satellite and airborne instruments, is shown to provide reliable cloud spectral emissivity and pressure level under a wide range of atmospheric conditions. The MLEV algorithm uses a physical approach in which the local variances of spectral cloud emissivity are calculated for a number of assumed or first-guess cloud pressure levels. The optimal solution for the single-layer cloud emissivity spectrum is that having the “minimum local emissivity variance” among the retrieved emissivity spectra associated with different first-guess cloud pressure levels. This is due to the fact that the absorption, reflection, and scattering processes of clouds exhibit relatively limited localized spectral emissivity structure in the infrared 10–15-μm longwave region. In this simulation study it is shown that the MLEV cloud pressure root-mean-square errors for a single level with effective cloud emissivity greater than 0.1 are ∼30, ∼10, and ∼50 hPa, for high (200– 300 hPa), middle (500 hPa), and low (850 hPa) clouds, respectively. The associated cloud emissivity root-mean-square errors in the 900 cm−1 spectral channel are less than 0.05, 0.04, and 0.25 for high, middle, and low clouds, respectively.

scholarly journals Optimizing UV index determination from broadband irradiances

2017 ◽  
Author(s):  
Keith A. Tereszchuk ◽  
Yves J. Rochon ◽  
Chris A. McLinden ◽  
Paul A. Vaillancourt
Keyword(s):  
Radiative Transfer ◽  
Root Mean Square ◽  
Uv Radiation ◽  
Relative Error ◽  
Spectral Resolution ◽  
High Spectral Resolution ◽  
Mean Square ◽  
Uv Index ◽  
Clear Sky ◽  
Sky Conditions

Abstract. Amidst mounting concerns about the depletion of stratospheric ozone (O3), and for subsequent increases in the surface irradiances of ultraviolet (UV) light and its effects on human health, a daily UV forecast program was launched by Environment Canada in 1993. The program serves to monitor harmful surface UV radiation and provide this information to the Canadian public through the UV index, a scale which reports the relative intensity of the Sun's UV radiation at the Earth's surface, and the corresponding protection actions to be taken. The UV index was accepted as a standard method of reporting surface UV irradiances by the World Meteorological Organization (WMO) and World Health Organization (WHO) in 1994. A study was undertaken to improve upon the prognosticative capability of Environment and Climate Change Canada's (ECCC) UV index forecast model. An aspect of that work, and the topic of this communication, was to investigate the use of the four UV broadband surface irradiance fields generated by ECCC's Global Environmental Multi-scale (GEM) numerical prediction model to determine the UV index. The basis of the investigation involves the creation of a suite of routines which employ high spectral resolution radiative transfer code developed to calculate UV index fields from GEM forecasts. These routines employ a modified version of the Cloud-J v7.4 radiative transfer model, which integrates GEM output to produce high spectral resolution surface irradiance fields. The output generated using the high-resolution radiative transfer code served to verify and calibrate GEM broadband surface irradiances under clear-sky conditions and their use in providing the UV index. A subsequent comparison of irradiances and UV index under cloudy conditions was also performed. Linear correlation agreement of surface irradiances from the two models for each of the two higher UV bands covering 310–330 nm and 330–400 nm is typically greater than 95 % for clear-sky conditions with associated root mean square relative errors of 5.5 % and 3.8 %. On the other hand, underestimations of clear-sky GEM irradiances were found on the order of ~30–50 % for the 294–310 nm band and by a factor of ~30 for the 280–294 nm band. This underestimation can be significant for UV index determination but would not impact weather forecasting. Corresponding empirical adjustments were applied to the broadband irradiances now giving a correlation coefficient of unity. From these, a least-squares fitting was derived for the calculation of the UV index. The resultant differences in UV indices from the high spectral resolution irradiances and the resultant GEM broadband irradiances are typically within 0.2 with a root mean square relative error in the scatter of ~5.5 % for clear-sky conditions. Similar results are reproduced under cloudy conditions with light to moderate clouds, having a relative error comparable to the clear-sky counterpart; under strong attenuation due to clouds, a substantial increase in the root mean square relative error of up to 30 % is observed due to differing cloud radiative transfer models.

scholarly journals Uncertainties of ground-based microwave radiometer retrievals in zenith and off-zenith methods under snow conditions

2016 ◽  
Author(s):  
Wengang Zhang ◽  
Guirong Xu ◽  
Yuanyuan Liu ◽  
Guopao Yan ◽  
Shengbo Wang
Keyword(s):  
Relative Humidity ◽  
Root Mean Square ◽  
Microwave Radiometer ◽  
Mean Square ◽  
Atmospheric Conditions ◽  
Vapor Density ◽  
Heavy Snowfall ◽  
Snow Conditions ◽  
The Impact ◽  
Mean Square Errors

Abstract. This paper is to investigate the uncertainties of microwave radiometer (MWR) retrievals in snow conditions and also explore the discrepancies of MWR retrievals in zenith and off-zenith methods. The MWR retrievals were averaged in the ±15 min period centered at sounding times of 00:00 and 12:00 UTC and compared with the radiosonde observations (RAOBs). In general, the MWR retrievals have a better correlation with RAOB profiles in off-zenith method than in zenith method, and the biases (MWR observations minus RAOBs) and root mean square errors (RMSEs) between MWR and RAOB are also clearly reduced in off-zenith method. The biases of temperature, relative humidity, and vapor density decrease from 4.6 K, 9 %, and 1.43 g m−3 in zenith method to −0.6 K, −2 %, and 0.10 g m−3 in off-zenith method, respectively. The discrepancies between the MWR retrievals and the RAOB profiles along with the altitude present the same situation. Case studies show that the impact of snow on accuracies of the MWR retrievals is more serious in heavy snowfall than that in light snowfall, but the off-zenith method can mitigate the impact of snowfall. The MWR measurements become less accurate in snowfall is mainly due to the retrieving method which does not consider the effect of snow, and the accumulated snow on the top of radome increases the signal noise of MWR measurement. As the snowfall drops away by gravity in the sides of the radome and the off-zenith observations are more representative of the atmospheric conditions for RAOBs.

Evaluation of the APSIM-Wheat model in terms of different cultivars, management regimes and environmental conditions

2012 ◽  
Vol 92 (5) ◽  
pp. 937-949 ◽  
Author(s):  
Yi Zhang ◽  
Liping Feng ◽  
Enli Wang ◽  
Jing Wang ◽  
Baoguo Li
Keyword(s):  
Root Mean Square ◽  
Environmental Conditions ◽  
Field Experiments ◽  
Growth And Yield ◽  
Planting Density ◽  
Mean Square ◽  
Sowing Dates ◽  
Wide Range ◽  
Management Regimes ◽  
Mean Square Errors

Zhang, Y., Feng, L., Wang, E., Wang, J. and Li, B. 2012. Evaluation of the APSIM-Wheat model in terms of different cultivars, management regimes and environmental conditions. Can. J. Plant Sci. 92: 937–949. Wheat is one of the most important crops in the world, and wheat models have been widely used to study yield responses to changes in management and climate. However, less information is available on how a wheat model performs in simulation of wheat response to changes in varieties, sowing dates and planting densities across space. This study presents an evaluation of the APSIM-Wheat model using data from field experiments consisting of three sowing dates, two and three crop varieties and three planting densities in a split-split plot design at three ecological sites from 2008 to 2010 in the North China Plain. The results show that the APSIM-Wheat model could capture a large part of the variation in phenology, biomass and yield for the same variety across sites. However, errors of simulation in phenology and yield were increased with delay in sowing date, with the average absolute root mean square errors of 2 d, 3 d, and 3–4 d in phenology, and the normalized root mean square error (RMSEn) of 7–12%, 11–16%, 16–22% in yield at early, medium, and late sowing dates, respectively. Simulation of yield achieved poor results with decreased planting density, with average RMSEn of 9–12%, 11–12%, and 16–19% at high, medium, and low density, respectively. Additionally, the simulation behaved in a complex manner, and the errors varied greatly with different combinations of sowing dates and planting densities. These alerted us that the model should be used cautiously to simulate growth and yield over a wide range of sowing dates and planting densities. Improved modeling of the responses of wheat growth to extreme temperatures during winter and spring periods, and to varying planting densities is needed for better future prediction. Other areas of model improvements are also discussed.

scholarly journals Development of observation-based global multilayer soil moisture products for 1970 to 2016

2021 ◽  
Vol 13 (9) ◽  
pp. 4385-4405
Author(s):  
Yaoping Wang ◽  
Jiafu Mao ◽  
Mingzhou Jin ◽  
Forrest M. Hoffman ◽  
Xiaoying Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Root Mean Square ◽  
Land Surface ◽  
Soil Layer ◽  
Added Value ◽  
Surface Model ◽  
Mean Square ◽  
Wide Range ◽  
Mean Square Errors

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m3 m−3, root mean square errors from 0.076 to 0.104 m3 m−3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

scholarly journals Optimizing UV Index determination from broadband irradiances

2018 ◽  
Vol 11 (3) ◽  
pp. 1093-1113 ◽  
Author(s):  
Keith A. Tereszchuk ◽  
Yves J. Rochon ◽  
Chris A. McLinden ◽  
Paul A. Vaillancourt
Keyword(s):  
Radiative Transfer ◽  
Root Mean Square ◽  
Relative Error ◽  
Spectral Resolution ◽  
High Spectral Resolution ◽  
Mean Square ◽  
Uv Index ◽  
Clear Sky ◽  
Surface Irradiance ◽  
Sky Conditions

Abstract. A study was undertaken to improve upon the prognosticative capability of Environment and Climate Change Canada's (ECCC) UV Index forecast model. An aspect of that work, and the topic of this communication, was to investigate the use of the four UV broadband surface irradiance fields generated by ECCC's Global Environmental Multiscale (GEM) numerical prediction model to determine the UV Index. The basis of the investigation involves the creation of a suite of routines which employ high-spectral-resolution radiative transfer code developed to calculate UV Index fields from GEM forecasts. These routines employ a modified version of the Cloud-J v7.4 radiative transfer model, which integrates GEM output to produce high-spectral-resolution surface irradiance fields. The output generated using the high-resolution radiative transfer code served to verify and calibrate GEM broadband surface irradiances under clear-sky conditions and their use in providing the UV Index. A subsequent comparison of irradiances and UV Index under cloudy conditions was also performed. Linear correlation agreement of surface irradiances from the two models for each of the two higher UV bands covering 310.70–330.0 and 330.03–400.00 nm is typically greater than 95 % for clear-sky conditions with associated root-mean-square relative errors of 6.4 and 4.0 %. However, underestimations of clear-sky GEM irradiances were found on the order of ∼ 30–50 % for the 294.12–310.70 nm band and by a factor of ∼ 30 for the 280.11–294.12 nm band. This underestimation can be significant for UV Index determination but would not impact weather forecasting. Corresponding empirical adjustments were applied to the broadband irradiances now giving a correlation coefficient of unity. From these, a least-squares fitting was derived for the calculation of the UV Index. The resultant differences in UV indices from the high-spectral-resolution irradiances and the resultant GEM broadband irradiances are typically within 0.2–0.3 with a root-mean-square relative error in the scatter of ∼ 6.6 % for clear-sky conditions. Similar results are reproduced under cloudy conditions with light to moderate clouds, with a relative error comparable to the clear-sky counterpart; under strong attenuation due to clouds, a substantial increase in the root-mean-square relative error of up to 35 % is observed due to differing cloud radiative transfer models.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

scholarly journals A coma-free super-high resolution optical spectrometer using 44 high dispersion sub-gratings

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hua-Tian Tu ◽  
An-Qing Jiang ◽  
Jian-Ke Chen ◽  
Wei-Jie Lu ◽  
Kai-Yan Zang ◽  
...  
Keyword(s):  
High Resolution ◽  
Spectral Resolution ◽  
Wavelength Region ◽  
Ultra Violet ◽  
High Spectral Resolution ◽  
Visible Range ◽  
High Dispersion ◽  
Charge Coupled Device ◽  
Wide Range ◽  
High Resolution Spectrometer

AbstractUnlike the single grating Czerny–Turner configuration spectrometers, a super-high spectral resolution optical spectrometer with zero coma aberration is first experimentally demonstrated by using a compound integrated diffraction grating module consisting of 44 high dispersion sub-gratings and a two-dimensional backside-illuminated charge-coupled device array photodetector. The demonstrated super-high resolution spectrometer gives 0.005 nm (5 pm) spectral resolution in ultra-violet range and 0.01 nm spectral resolution in the visible range, as well as a uniform efficiency of diffraction in a broad 200 nm to 1000 nm wavelength region. Our new zero-off-axis spectrometer configuration has the unique merit that enables it to be used for a wide range of spectral sensing and measurement applications.

scholarly journals Uncertainties of ground-based microwave radiometer retrievals in zenith and off-zenith observations under snow conditions

2017 ◽  
Vol 10 (1) ◽  
pp. 155-165 ◽  
Author(s):  
Wengang Zhang ◽  
Guirong Xu ◽  
Yuanyuan Liu ◽  
Guopao Yan ◽  
Dejun Li ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Microwave Radiometer ◽  
Retrieval Algorithm ◽  
Mean Square ◽  
Atmospheric Conditions ◽  
Vapor Density ◽  
Heavy Snowfall ◽  
Snow Conditions ◽  
The Impact ◽  
Mean Square Errors

Abstract. This paper is to investigate the uncertainties of microwave radiometer (MWR) retrievals in snow conditions and also explore the discrepancies of MWR retrievals in zenith and off-zenith observations. The MWR retrievals were averaged in a ±15 min period centered at sounding times of 00:00 and 12:00 UTC and compared with radiosonde observations (RAOBs). In general, the MWR retrievals have a better correlation with RAOB profiles in off-zenith observations than in zenith observations, and the biases (MWR observations minus RAOBs) and root mean square errors (RMSEs) between MWR and RAOB are also clearly reduced in off-zenith observations. The biases of temperature, relative humidity, and vapor density decrease from 4.6 K, 9 %, and 1.43 g m−3 in zenith observations to −0.6 K, −2 %, and 0.10 g m−3 in off-zenith observations, respectively. The discrepancies between MWR retrievals and RAOB profiles by altitude present the same situation. Cases studies show that the impact of snow on accuracies of MWR retrievals is more serious in heavy snowfall than in light snowfall, but off-zenith observation can mitigate the impact of snowfall. The MWR measurements become less accurate in snowfall mainly due to the retrieval algorithm, which does not consider the effect of snow, and the accumulated snow on the top of the radome increases the signal noise of MWR measurements. As the snowfall drops away by gravity on the sides of the radome, the off-zenith observations are more representative of the atmospheric conditions for RAOBs.

scholarly journals Evidence That Supertriangles Exist in Nature from the Vertical Projections of Koelreuteria paniculata Fruit

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 23
Author(s):  
Yuping Li ◽  
Brady K. Quinn ◽  
Johan Gielis ◽  
Yirong Li ◽  
Peijian Shi
Keyword(s):  
Root Mean Square ◽  
Goodness Of Fit ◽  
Mean Square ◽  
Sea Stars ◽  
Different Types ◽  
Koelreuteria Paniculata ◽  
Two Parameters ◽  
Mean Square Errors

Many natural radial symmetrical shapes (e.g., sea stars) follow the Gielis equation (GE) or its twin equation (TGE). A supertriangle (three triangles arranged around a central polygon) represents such a shape, but no study has tested whether natural shapes can be represented as/are supertriangles or whether the GE or TGE can describe their shape. We collected 100 pieces of Koelreuteria paniculata fruit, which have a supertriangular shape, extracted the boundary coordinates for their vertical projections, and then fitted them with the GE and TGE. The adjusted root mean square errors (RMSEadj) of the two equations were always less than 0.08, and >70% were less than 0.05. For 57/100 fruit projections, the GE had a lower RMSEadj than the TGE, although overall differences in the goodness of fit were non-significant. However, the TGE produces more symmetrical shapes than the GE as the two parameters controlling the extent of symmetry in it are approximately equal. This work demonstrates that natural supertriangles exist, validates the use of the GE and TGE to model their shapes, and suggests that different complex radially symmetrical shapes can be generated by the same equation, implying that different types of biological symmetry may result from the same biophysical mechanisms.

scholarly journals Differential absorption lidar measurements of water vapor by the High Altitude Lidar Observatory (HALO): Retrieval framework and validation

2021 ◽  
Author(s):  
Brian J. Carroll ◽  
Amin R. Nehrir ◽  
Susan Kooi ◽  
James Collins ◽  
Rory A. Barton-Grimley ◽  
...  
Keyword(s):  
Water Vapor ◽  
High Altitude ◽  
Dynamic Range ◽  
Precipitable Water ◽  
Spatiotemporal Variability ◽  
High Spectral Resolution ◽  
Atmospheric Conditions ◽  
Differential Absorption ◽  
Differential Absorption Lidar ◽  
Wide Range

Abstract. Airborne differential absorption lidar (DIAL) offers a uniquely capable solution to the problem of measuring water vapor (WV) with high precision, accuracy, and resolution throughout the troposphere and lower stratosphere. The High Altitude Lidar Observatory (HALO) airborne WV DIAL was recently developed at NASA Langley Research Center and was first deployed in 2019. It uses four wavelengths at 935 nm to achieve sensitivity over a wide dynamic range, and simultaneously employs 1064 nm backscatter and 532 nm high spectral resolution lidar (HSRL) measurements for aerosol and cloud profiling. A key component of the WV retrieval framework is flexibly trading resolution for precision to achieve optimal data sets for scientific objectives across scales. A technique for retrieving WV in the lowest few hundred meters of the atmosphere using the strong surface return signal is also presented. The five maiden flights of the HALO WV DIAL spanned the tropics through midlatitudes with a wide range of atmospheric conditions, but opportunities for validation were sparse. Comparisons to dropsonde WV profiles were qualitatively in good agreement, though statistical analysis was impossible due to systematic error in the dropsonde measurements. Comparison of HALO to in situ WV measurements onboard the aircraft showed no substantial bias across three orders of magnitude, despite variance (R2 = 0.66) that may be largely attributed to spatiotemporal variability. Precipitable water vapor measurements from the spaceborne sounders AIRS and IASI compared very well to HALO with R2 > 0.96 over ocean and R2 = 0.86 over land.

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