scholarly journals Validation of satellite OPEMW precipitation product with ground-based weather radar and rain gauge networks

2013 ◽  
Vol 6 (3) ◽  
pp. 4279-4312 ◽  
Author(s):  
D. Cimini ◽  
F. Romano ◽  
E. Ricciardelli ◽  
F. Di Paola ◽  
M. Viggiano ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Winter Season ◽  
National Research Council ◽  
Residual Effect ◽  
Temporal Analysis ◽  
Mean Difference ◽  
Absolute Difference ◽  
Data Set ◽  
Rain Gauges ◽  
Weather Radars

Abstract. The Precipitation Estimation at Microwave Frequencies (PEMW) algorithm was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) for inferring surface rain intensity (sri) from satellite passive microwave observations in the range from 89 to 190 GHz. The operational version of PEMW (OPEMW) has been running continuously at IMAA-CNR for two years, producing sri estimates feeding an operational hydrological model for forecasting flood alerts. This paper presents the validation of OPEMW against simultaneous ground-based observations obtained by a network of 20 weather radars and a network of more than 3000 rain gauges distributed over the Italian peninsula and main islands. The validation effort uses a data set spanning a one-year period (July 2011–June 2012). The effort evaluates dichotomous and continuous scores for the assessment of rain detection and quantitative estimate, respectively, investigating both spatial and temporal features. The analysis demonstrates 98% accuracy in correctly identifying rainy and non-rainy areas, and it quantifies the increased ability (with respect to random chance) to detect rainy and non-rainy areas (0.42–0.45 Heidke skill score) or rainy areas only (0.27–0.29 equitable threat score). Performances are better than average during summer, fall, and spring, while worse than average in the winter season. The spatial-temporal analysis does not show seasonal dependence except for larger mean absolute difference over the Alps and northern Apennines during winter, attributable to residual effect of snow cover. A binned analysis in the 0–15 mm h−1 range suggests that OPEMW tends to slightly overestimate sri values below 6–7 mm h−1, and to underestimate sri above those values. Depending upon the ground reference (either rain gauges or weather radars), the mean difference is 0.8–2.8 mm h−1, with a standard deviation within 2.6–3.1 mm h−1 and correlation coefficient within 0.8–0.9. The monthly mean difference was shown to remain within ±1 mm h−1 with respect to rain gauges and within −2 mm h−1 with respect to weather radars, with 2–4 mm h−1 standard deviation.

scholarly journals Validation of satellite OPEMW precipitation product with ground-based weather radar and rain gauge networks

2013 ◽  
Vol 6 (11) ◽  
pp. 3181-3196 ◽  
Author(s):  
D. Cimini ◽  
F. Romano ◽  
E. Ricciardelli ◽  
F. Di Paola ◽  
M. Viggiano ◽  
...  
Keyword(s):  
Winter Season ◽  
National Research Council ◽  
Weather Radar ◽  
Temporal Analysis ◽  
Skill Score ◽  
Rain Gauge ◽  
Set Covering ◽  
Data Set ◽  
Rain Gauges ◽  
Weather Radars

Abstract. The Precipitation Estimation at Microwave Frequencies (PEMW) algorithm was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) for inferring surface rain intensity (sri) from satellite passive microwave observations in the range from 89 to 190 GHz. The operational version of PEMW (OPEMW) has been running continuously at IMAA-CNR for two years. The OPEMW sri estimates, together with other precipitation products, are used as input to an operational hydrological model for flood alert forecast. This paper presents the validation of OPEMW against simultaneous ground-based observations from a network of 20 weather radar systems and a network of more than 3000 rain gauges distributed over the Italian Peninsula and main islands. The validation effort uses a data set covering one year (July 2011–June 2012). The effort evaluates dichotomous and continuous scores for the assessment of rain detection and quantitative estimate, respectively, investigating both spatial and temporal features. The analysis demonstrates 98% accuracy in correctly identifying rainy and non-rainy areas; it also quantifies the increased ability (with respect to random chance) to detect rainy and non-rainy areas (0.42–0.45 Heidke skill score) or rainy areas only (0.27–0.29 equitable threat score). Performances are better than average during summer, fall, and spring, while worse than average in the winter season. The spatial–temporal analysis does not show seasonal dependence except over the Alps and northern Apennines during winter. A binned analysis in the 0–15 mm h−1 range suggests that OPEMW tends to slightly overestimate sri values below 6–7 mm h−1 and underestimate sri above those values. With respect to rain gauges (weather radars), the correlation coefficient is larger than 0.8 (0.9). The monthly mean difference and standard deviation remain within ±1 and 2 mm h−1 with respect to rain gauges (respectively −2–0 and 4 mm h−1 with respect to weather radars).

scholarly journals Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

2015 ◽  
Vol 8 (8) ◽  
pp. 8191-8230 ◽  
Author(s):  
A. Overeem ◽  
H. Leijnse ◽  
R. Uijlenhoet
Keyword(s):  
Communication Networks ◽  
Cellular Communication ◽  
Retrieval Algorithm ◽  
Data Set ◽  
Rain Gauges ◽  
Continental Scale ◽  
Weather Radars ◽  
Cellular Communication Network ◽  
Scripting Language ◽  
Areal Rainfall

Abstract. Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide rainfall maps can be derived from the signal attenuations of microwave links in such a network. Here we give a detailed description of the employed rainfall retrieval algorithm and provide the corresponding code. Moreover, the code (in the scripting language "R") is made available including a data set of commercial microwave links. The purpose of this paper is to promote rainfall monitoring utilizing microwave links from cellular communication networks as an alternative or complementary means for global, continental-scale rainfall monitoring.

scholarly journals Technical note: analytical estimation of the optimal parameters for the EOF retrievals of the IASI Level 2 Product Processing Facility and its application using AIRS and ECMWF data

2006 ◽  
Vol 6 (3) ◽  
pp. 831-846 ◽  
Author(s):  
X. Calbet ◽  
P. Schlüssel
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Mean Difference ◽  
Training Data ◽  
Data Set ◽  
The Real ◽  
Atmospheric Profiles ◽  
The Mean ◽  
Atmospheric State ◽  
The Difference

Abstract. The Empirical Orthogonal Function (EOF) retrieval technique consists of calculating the eigenvectors of the spectra to later perform a linear regression between these and the atmospheric states, this first step is known as training. At a later stage, known as performing the retrievals, atmospheric profiles are derived from measured atmospheric radiances. When EOF retrievals are trained with a statistically different data set than the one used for retrievals two basic problems arise: significant biases appear in the retrievals and differences between the covariances of the training data set and the measured data set degrade them. The retrieved profiles will show a bias with respect to the real profiles which comes from the combined effect of the mean difference between the training and the real spectra projected into the atmospheric state space and the mean difference between the training and the atmospheric profiles. The standard deviations of the difference between the retrieved profiles and the real ones show different behavior depending on whether the covariance of the training spectra is bigger, equal or smaller than the covariance of the measured spectra with which the retrievals are performed. The procedure to correct for these effects is shown both analytically and with a measured example. It consists of first calculating the average and standard deviation of the difference between real observed spectra and the calculated spectra obtained from the real atmospheric state and the radiative transfer model used to create the training spectra. In a later step, measured spectra must be bias corrected with this average before performing the retrievals and the linear regression of the training must be performed adding noise to the spectra corresponding to the aforementioned calculated standard deviation. This procedure is optimal in the sense that to improve the retrievals one must resort to using a different training data set or a different algorithm.

scholarly journals Technical note: analytical estimation of the optimal parameters for the EOF retrievals of the IASI Level 2 Product Processing Facility and its application using AIRS and ECMWF data

2005 ◽  
Vol 5 (5) ◽  
pp. 9691-9730
Author(s):  
X. Calbet ◽  
P. Schlüssel
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Mean Difference ◽  
Training Data ◽  
Data Set ◽  
The Real ◽  
Atmospheric Profiles ◽  
The Mean ◽  
Atmospheric State ◽  
The Difference

Abstract. The Empirical Orthogonal Function (EOF) retrieval technique consists of calculating the eigenvectors of the spectra to later perform a linear regression between these and the atmospheric states, this first step is known as training. At a later stage, known as performing the retrievals, atmospheric profiles are derived from measured atmospheric radiances. When EOF retrievals are trained with a statistically different data set than the one used for retrievals two basic problems arise: significant biases appear in the retrievals and differences between the covariances of the training data set and the measured data set degrade them. The retrieved profiles will show a bias with respect to the real profiles which comes from the combined effect of the mean difference between the training and the real spectra projected into the atmospheric state space and the mean difference between the training and the atmospheric profiles. The standard deviations of the difference between the retrieved profiles and the real ones show different behavior depending on whether the covariance of the training spectra is bigger, equal or smaller than the covariance of the measured spectra with which the retrievals are performed. The procedure to correct for these effects is shown both analytically and with a measured example. It consists of first calculating the average and standard deviation of the difference between real observed spectra and the calculated spectra obtained from the real atmospheric state and the radiative transfer model used to create the training spectra. In a later step, measured spectra must be bias corrected with this average before performing the retrievals and the linear regression of the training must be performed adding noise to the spectra corresponding to the aforementioned calculated standard deviation. This procedure is optimal in the sense that to improve the retrievals one must resort to using a different training data set or a different algorithm.

scholarly journals Validation of Scolioscan Air-Portable Radiation-Free Three-Dimensional Ultrasound Imaging Assessment System for Scoliosis

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2858
Author(s):  
Kelly Ka-Lee Lai ◽  
Timothy Tin-Yan Lee ◽  
Michael Ka-Shing Lee ◽  
Joseph Chi-Ho Hui ◽  
Yong-Ping Zheng
Keyword(s):  
Ultrasound Imaging ◽  
Imaging System ◽  
Outcome Measurement ◽  
Three Dimensional ◽  
3D Ultrasound ◽  
Mean Difference ◽  
Assessment System ◽  
Absolute Difference ◽  
Data Sets ◽  
Low Profile

To diagnose scoliosis, the standing radiograph with Cobb’s method is the gold standard for clinical practice. Recently, three-dimensional (3D) ultrasound imaging, which is radiation-free and inexpensive, has been demonstrated to be reliable for the assessment of scoliosis and validated by several groups. A portable 3D ultrasound system for scoliosis assessment is very much demanded, as it can further extend its potential applications for scoliosis screening, diagnosis, monitoring, treatment outcome measurement, and progress prediction. The aim of this study was to investigate the reliability of a newly developed portable 3D ultrasound imaging system, Scolioscan Air, for scoliosis assessment using coronal images it generated. The system was comprised of a handheld probe and tablet PC linking with a USB cable, and the probe further included a palm-sized ultrasound module together with a low-profile optical spatial sensor. A plastic phantom with three different angle structures built-in was used to evaluate the accuracy of measurement by positioning in 10 different orientations. Then, 19 volunteers with scoliosis (13F and 6M; Age: 13.6 ± 3.2 years) with different severity of scoliosis were assessed. Each subject underwent scanning by a commercially available 3D ultrasound imaging system, Scolioscan, and the portable 3D ultrasound imaging system, with the same posture on the same date. The spinal process angles (SPA) were measured in the coronal images formed by both systems and compared with each other. The angle phantom measurement showed the measured angles well agreed with the designed values, 59.7 ± 2.9 vs. 60 degrees, 40.8 ± 1.9 vs. 40 degrees, and 20.9 ± 2.1 vs. 20 degrees. For the subject tests, results demonstrated that there was a very good agreement between the angles obtained by the two systems, with a strong correlation (R2 = 0.78) for the 29 curves measured. The absolute difference between the two data sets was 2.9 ± 1.8 degrees. In addition, there was a small mean difference of 1.2 degrees, and the differences were symmetrically distributed around the mean difference according to the Bland–Altman test. Scolioscan Air was sufficiently comparable to Scolioscan in scoliosis assessment, overcoming the space limitation of Scolioscan and thus providing wider applications. Further studies involving a larger number of subjects are worthwhile to demonstrate its potential clinical values for the management of scoliosis.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

scholarly journals MAX-DOAS observations of aerosols, formaldehyde and nitrogen dioxide in the Beijing area: comparison of two profile retrieval approaches

2015 ◽  
Vol 8 (2) ◽  
pp. 941-963 ◽  
Author(s):  
T. Vlemmix ◽  
F. Hendrick ◽  
G. Pinardi ◽  
I. De Smedt ◽  
C. Fayt ◽  
...  
Keyword(s):  
Standard Deviation ◽  
A Priori ◽  
Systematic Bias ◽  
Aerosol Extinction ◽  
Data Set ◽  
Near Surface ◽  
Beijing Area ◽  
Least Squares Fit ◽  
Relative Differences ◽  
Good Agreement

Abstract. A 4-year data set of MAX-DOAS observations in the Beijing area (2008–2012) is analysed with a focus on NO2, HCHO and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2–4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols) the retrieval outcomes are compared in terms of tropospheric column densities, surface concentrations and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column density resides). We find best agreement between the two methods for tropospheric NO2 column densities, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO column densities we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs) retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ~ 25%). With respect to near-surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30, −23 ± 28 and −8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method A are very close to the a priori, and moderate for NO2 and aerosol extinction which on average show quite good agreement for characteristic profile heights below 1.5 km. One of the main advantages of method A is the stability, even under suboptimal conditions (e.g. in the presence of clouds). Method B is generally more unstable and this explains probably a substantial part of the quite large relative differences between the two methods. However, despite a relatively low precision for individual profile retrievals it appears as if seasonally averaged profile heights retrieved with method B are less biased towards a priori assumptions than those retrieved with method A. This gives confidence in the result obtained with method B, namely that aerosol extinction profiles tend on average to be higher than NO2 profiles in spring and summer, whereas they seem on average to be of the same height in winter, a result which is especially relevant in relation to the validation of satellite retrievals.

Gestational and lactational feeding strategies for gilts: Growth, carcass characteristics and meat quality of the progeny

2003 ◽  
Vol 83 (2) ◽  
pp. 205-211 ◽  
Author(s):  
A. Fortin ◽  
E. J. Clowes ◽  
A. L. Schaefer
Keyword(s):  
Meat Quality ◽  
National Research Council ◽  
Carcass Characteristics ◽  
Residual Effect ◽  
Feeding Strategies ◽  
Pork Quality ◽  
National Academy Of Sciences ◽  
Weaning Weight ◽  
Body Protein ◽  
Academy Of Sciences

This study was conducted to determine whether feeding gilts (1) at or above their National Academy of Sciences-National Research Council (NAS-NRC 1998) requirements during gestation, and (2) to lose a moderate (~10%) or large (~17%) amount of maternal protein during lactation had a residual effect on their progeny’s growth, carcass characteristics and pork quality at market weight. From each litter, the heaviest and lightest barrows and gilts were selected. The progeny of gilts fed above their requirements during gestation, and those that lost the least body protein during lactation were heavier at weaning; +0.3 kg (P < 0.05) and +0.5 kg (P = 0.01), respectively. However, these liveweight differences, which were associated with the gestation and lactation effects, were no longer evident (P > 0.05) at day 35 or 85 post-weaning. But at slaughter, these animals had thinner (P < 0.01) fat thickness and higher (P < 0.05) predicted salable meat yield. Independently of the gestation and lactation treatments, and compared to the low-weaning-weight pigs, the high- weaning-weight pigs maintained their weight advantage (P < 0.01 at day 35 (+ 2.8 kg) and day 85 (+ 5.4 kg) post-weaning), took 4.5 fewer days (P < 0.01) to reach market weight, but had similar (P > 0.05) carcass characteristics and pork quality. Key words: Gilts, gestational and lactational protein, litter, growth, carcass characteristics and meat quality

Comparison of supplemental cobalt form on fibre digestion and cobalamin concentrations in cattle

2017 ◽  
Vol 155 (5) ◽  
pp. 832-838 ◽  
Author(s):  
R. C. WATERMAN ◽  
W. L. KELLY ◽  
C. K. LARSON ◽  
M. K. PETERSEN
Keyword(s):  
Crude Protein ◽  
Dry Matter ◽  
National Research Council ◽  
Residual Effect ◽  
Vitamin B ◽  
Serum Concentrations ◽  
Forage Digestibility ◽  
Randomized Experimental Design ◽  
To Receive

SUMMARYCobalt (Co) is essential for rumen microbial metabolism to synthesize methane, acetate and methionine. It also serves as a structural component of vitamin B12(cobalamin), which functions as a coenzyme in energy metabolism. A study was conducted to determine if Co form (carbonatev. glucoheptonate) supplemented above the National Research Council requirements would improve digestibility of a low-quality forage diet and change serum cobalamin concentrations. Nineteen ruminally cannulated cows (577 ± 13 kg) were fed individually in a completely randomized experimental design. Cows were fed a grass hay diet that contained (79·2 g/kg crude protein, 565 g/kg total digestible nutrients, 633·2 g/kg neutral detergent fibre (NDF), 874·2 g/kg dry matter) at a rate of 0·02% of body weight on a as fed basis for a 62-day study, which consisted of three periods; acclimation (AC), treatment (TR) and residual (RE). Measurements taken in the AC period were used as covariates for analysis in the TR and RE periods. Cows were stratified by age (5 ± 0·4 years) and lactational history, and assigned to receive 12·5 mg supplemental Co in one of two forms: (1) 27·2 mg of Co carbonate (CC,n= 11 cows) or (2) 50 mg of Co glucoheptonate (CGH,n= 8 cows). Supplement was administered daily via a gelatin capsule placed directly into the rumen 2 h after feeding. During the last 96 h of each period, forage digestibility was measured using anin situnylon bag technique. Blood samples were collected 4 and 6 h following feeding, and 24 h before the end of each period. A treatment × period interaction was detected forin situorganic matter (OM) disappearance at 96 h; (TR period: 684 and 708 ± 81 g/kg; RE period: 676 and 668 ± 75 g/kg, for CC and CGH, respectively). Once inclusion of Co in the CGH group was removed, OM disappearance was reduced by 4·01% compared with 0·82% in the CC cows. The NDF disappearance (OM basis) was less for the TR compared with the RE at 48 h (629 and 652 ± 39 g/kg, respectively). However, by 96 h NDF disappearance was greater for TR than the RE (704 and 689 ± 44 g/kg; respectively). No differences were detected for cobalamin serum concentrations or rate of fibre fermentation. The outcomes of the current research signify that there may be a slight residual effect of Co supplementation on fermentation; there was also an indication that Co source may enhance the overall extent of fermentation.

scholarly journals Analysis of Catania Flash Flood Case Study by Using Combined Microwave and Infrared Technique

2014 ◽  
Vol 15 (5) ◽  
pp. 1989-1998 ◽  
Author(s):  
Francesco Di Paola ◽  
Elisabetta Ricciardelli ◽  
Domenico Cimini ◽  
Filomena Romano ◽  
Mariassunta Viggiano ◽  
...  
Keyword(s):  
Real Time ◽  
Flash Flood ◽  
Weather Prediction ◽  
Rapid Evolution ◽  
Cell Formation ◽  
Winter Season ◽  
National Research Council ◽  
Severe Weather ◽  
Synoptic Situation

Abstract In this paper, the analysis of an extreme convective event atypical for the winter season, which occurred on 21 February 2013 on the east coast of Sicily and caused a flash flood over Catania, is presented. In just 1 h, more than 50 mm of precipitation was recorded, but it was not forecast by numerical weather prediction (NWP) models and, consequently, no severe weather warnings were sent to the population. The case study proposed is first examined with respect to the synoptic situation and then analyzed by means of two algorithms based on satellite observations: the Cloud Mask Coupling of Statistical and Physical Methods (MACSP) and the Precipitation Evolving Technique (PET), developed at the National Research Council of Italy. Both of the algorithms show their ability in the near-real-time monitoring of convective cell formation and their rapid evolution. As quantitative precipitation forecasts by NWP could fail, especially for atypical convective events like in Catania, tools like MACSP and PET shall be adopted by civil protection centers to monitor the real-time evolution of deep convection events in aid to the severe weather warning service.

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