Root-Mean-Square (RMS) Values of In-Situ Parameters in Air-Water Heterogeneous Mixture Flow in a Horizontal Minichannel

Due to the smaller ground footprint and higher spatial resolution of the Synthetic Aperture Radar (SAR) mode, altimeter observations from the Sentinel-3 satellites are expected to be overall more accurate in coastal areas than conventional nadir altimetry. The performance of Sentinel-3A in the coastal region of southwest England was assessed by comparing SAR mode observations of significant wave height against those of Pseudo Low Resolution Mode (PLRM). Sentinel-3A observations were evaluated against in-situ observations from a network of 17 coastal wave buoys, which provided continuous time-series of hourly values of significant wave height, period and direction. As the buoys are evenly distributed along the coast of southwest England, they are representative of a broad range of morphological configurations and swell conditions against which to assess Sentinel-3 SAR observations. The analysis indicates that SAR observations outperform PLRM within 15 km from the coast. Within that region, regression slopes between SAR and buoy observations are close to the 1:1 relation, and the average root mean square error between the two is 0.46 ± 0.14 m. On the other hand, regression slopes for PLRM observations rapidly deviate from the 1:1 relation, while the average root mean square error increases to 0.84 ± 0.45 m. The analysis did not identify any dependence of the bias between SAR and in-situ observation on the swell period or direction. The validation is based on a synergistic approach which combines satellite and in-situ observations with innovative use of numerical wave model output to help inform the choice of comparison regions. Such an approach could be successfully applied in future studies to assess the performance of SAR observations over other combinations of coastal regions and altimeters.

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Quantifying connectivity uncertainty arising from circulation modelling inaccuracy.

10.5194/egusphere-egu2020-2428 ◽

2020 ◽

Author(s):

Elise Vissenaekens ◽

Katell Guizien

Keyword(s):

Root Mean Square ◽

Flow Speed ◽

Population Connectivity ◽

Mean Square ◽

Statistical Parameters ◽

Ocean Modelling ◽

Gulf Of Lion ◽

Study Population ◽

In Situ Observations

Ocean modelling has become an increasingly important tool to study population connectivity and is our only tool to anticipate changes in dispersal routes in future climates. To estimate the uncertainties in model predictions, a comparison was made between the simulated currents and in situ observations in the Gulf of Lion over the period of 2009-2013. The uncertainties in Eulerian current values were described using several statistical parameters, like the bias, the root mean square (RMSE), the naturalised root mean square (NRMSE), the Hannah and Heinold parameter (HH) and the correlation. Another parameter that was introduced was the correctness, which states the percentage of time the model was deemed &#8220;correct&#8221;, based on low HH values (<75%) and high correlation (>0.25). So far, the model simulated the flow speed correctly 60-70% of the time and the relative deviation between observed and simulated flow speed was about 10%. Furthermore, ensembles of Lagrangian tracks were simulated accounting for uncertainties in Eulerian flow speed. These uncertainties were either correlated to speed values or chosen according to their statistical distribution. The Lagrangian tracks were analysed to construct connectivity matrices with and without these Eulerian uncertainties. Resulting deviation in retention and larval transfer arising from flow speed uncertainty were quantified.

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Pressure gradient calculation for air–water heterogeneous mixture flow in a small square horizontal channel based on the in situ parameters and flow pattern coefficient

Experimental Thermal and Fluid Science ◽

10.1016/j.expthermflusci.2005.04.003 ◽

2006 ◽

Vol 30 (4) ◽

pp. 403-413 ◽

Cited By ~ 2

Author(s):

Jerry K. Keska ◽

Gongtao Wang

Keyword(s):

Pressure Gradient ◽

Flow Pattern ◽

Horizontal Channel ◽

Heterogeneous Mixture ◽

Mixture Flow ◽

Gradient Calculation

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In-situ monitoring of saccharides removal of alcohol precipitation using near-infrared spectroscopy

Journal of Innovative Optical Health Sciences ◽

10.1142/s179354581850027x ◽

2018 ◽

Vol 11 (05) ◽

pp. 1850027 ◽

Cited By ~ 2

Author(s):

Hongxia Huang ◽

Haibin Qu

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Process Analytical Technology ◽

Nir Spectroscopy ◽

In Situ Monitoring ◽

Mean Square ◽

Model Errors ◽

Precipitation Processes ◽

Mean Square Errors

As unsafe components in herbal medicine (HM), saccharides can affect not only the drug appearance and stabilization, but also the drug efficacy and safety. The present study focuses on the in-line monitoring of batch alcohol precipitation processes for saccharide removal using near-infrared (NIR) spectroscopy. NIR spectra in the 4000–10,000-cm[Formula: see text] wavelength range are acquired in situ using a transflectance probe. These directly acquired spectra allow characterization of the dynamic variation tendency of saccharides during alcohol precipitation. Calibration models based on partial least squares (PLS) regression have been developed for the three saccharide impurities, namely glucose, fructose, and sucrose. Model errors are estimated as the root-mean-square errors of cross-validation (RMSECVs) of internal validation and root-mean-square errors of prediction (RMSEPs) of external validation. The RMSECV values of glucose, fructose, and sucrose were 1.150, 1.535, and 3.067[Formula: see text]mg[Formula: see text]mL[Formula: see text], and the RMSEP values were 0.711, 1.547, and 3.740[Formula: see text][Formula: see text], respectively. The correlation coefficients [Formula: see text] between the NIR predictive and the reference measurement values were all above 0.94. Furthermore, NIR predictions based on the constructed models improved our understanding of sugar removal and helped develop a control strategy for alcohol precipitation. The results demonstrate that, as an alternative process analytical technology (PAT) tool for monitoring batch alcohol precipitation processes, NIR spectroscopy is advantageous for both efficient determination of quality characteristics (fast, in situ, and requiring no toxic reagents) and process stability, and evaluating the repeatability.

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Pola Distribusi Konsentrasi Klorofil-a di Laut Maluku berdasarkan Pengamatan In Situ INDESO Joint Expedition Program 2016 dan Data Penginderaan Jauh

Jurnal Segara ◽

10.15578/segara.v13i3.6494 ◽

2017 ◽

Vol 13 (2) ◽

Author(s):

Indra Hermawan ◽

Agus Setiawan ◽

Nikita Pusparini

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mixed Layer ◽

Mean Square

Tujuan dari penelitian ini adalah untuk mengetahui pola distribusi konsentrasi klorofil-a di perairan Laut Maluku yang termasuk dalam Wilayah Pengelolaan Perikanan RI 715 berdasarkan data pengamatan in situ dan penginderaan jauh. Penelitian ini dilakukan pada bulan September 2016 dan merupakan bagian dari Pelayaran Oseanografi INDESO Joint Expedition Program (IJEP) 2016 yang dilaksanakan oleh Badan Penelitian dan Pengembangan Kelautan dan Perikanan (Balitbang KP) menggunakan Kapal Riset Baruna Jaya VIII. Berdasarkan hasil pengamatan in situ pada 8 titik pengamatan di sepanjang Laut Maluku dari selatan ke utara, didapatkan bahwa konsentrasi klorofil-a di permukaan berkisar antara 0,1 hingga 0,6 mg/m3. Hasil perbandingan antara konsentrasi klorofil-a hasil pengamatan in-situ dengan model biogeokimia INDESO dan citra satelit SeaWiFS masing-masing memberikan root mean square error sebesar 0,1507 dan 0,1364 mg/m3. Sementara itu, secara vertikal konsentrasi klorofil-a maksimum (antara 0,4 hingga 1 mg/m3) ditemukan pada kedalaman antara 17 hingga 61 meter, yaitu pada lapisan mixed layer.

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Evaluation of Remotely-Sensed and Model-Based Soil Moisture Products According to Different Soil Type, Vegetation Cover and Climate Regime Using Station-Based Observations over Turkey

Remote Sensing ◽

10.3390/rs11161875 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1875 ◽

Cited By ~ 2

Author(s):

Burak Bulut ◽

M. Tugrul Yilmaz ◽

Mehdi H. Afshar ◽

A. Ünal Şorman ◽

İsmail Yücel ◽

...

Keyword(s):

Soil Moisture ◽

Root Mean Square ◽

Soil Type ◽

Vegetation Cover ◽

Remotely Sensed ◽

Mean Square ◽

Climate Regime ◽

Model Based ◽

In Situ Observations

This study evaluates the performance of widely-used remotely sensed- and model-based soil moisture products, including: The Advanced Scatterometer (ASCAT), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the European Space Agency Climate Change Initiative (ESA-CCI), the Antecedent Precipitation Index (API), and the Global Land Data Assimilation System (GLDAS-NOAH). Evaluations are performed between 2008 and 2011 against the calibrated station-based soil moisture observations collected by the General Directorate of Meteorology of Turkey. The calibration of soil moisture observing sensors with respect to the soil type, correction of the soil moisture for the soil temperature, and the quality control of the collected measurements are performed prior to the evaluation of the products. Evaluation of remotely sensed- and model-based soil moisture products is performed considering different characteristics of the time series (i.e., seasonality and anomaly components) and the study region (i.e., soil type, vegetation cover, soil wetness and climate regime). The systematic bias between soil moisture products and in situ measurements is eliminated by using a linear rescaling method. Correlations between the soil moisture products and the in situ observations vary between 0.57 and 0.87, while the root mean square errors of the products versus the in situ observations vary between 0.028 and 0.043 m3 m−3. Overall, according to the correlation and root mean square error values obtained in all evaluation categories, NOAH and ESA-CCI soil moisture products perform better than all the other model- and remotely sensed-based soil moisture products. These results are valid for the entire study time period and all of the sub-categories under soil type, vegetation cover, soil wetness and climate regime.

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

Earth System Science Data ◽

10.5194/essd-13-4385-2021 ◽

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.

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UJI AKURASI PRODUK ESTIMASI CURAH HUJAN SATELIT GPM IMERG DI SURABAYA, INDONESIA

Jurnal Sains & Teknologi Modifikasi Cuaca ◽

10.29122/jstmc.v19i2.3153 ◽

2018 ◽

Vol 19 (2) ◽

pp. 83

Author(s):

Mukhamad Adib Azka ◽

Prabu Aditya Sugianto ◽

Andreas Kurniawan Silitonga ◽

Imma Redha Nugraheni

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mean Absolute Error ◽

Absolute Error ◽

Mean Square

Curah hujan merupakan parameter meteorologi yang sangat berpengaruh dalam kehidupan. Saat ini, pengamatan secara in situ sangat kurang representatif untuk digunakan sebagai analisis karena jangkauannya yang sangat sempit sehingga memerlukan instrumen pendukung seperti satelit agar dapat memberikan gambaran yang lebih baik terkait distribusi hujan. Namun, data satelit juga belum tentu sepenuhnya benar karena resolusi dan kondisi dari setiap wilayah berbeda. Penelitian ini bertujuan untuk mendapatkan nilai akurasi, bias, korelasi, root mean square error (RMSE), dan mean absolute error (MAE) data estimasi curah hujan GPM IMERG dengan data curah hujan pengamatan langsung. Penelitian ini dilakukkan di Surabaya dengan menggunakan data estimasi curah hujan GPM IMERG dan data curah hujan pengamatan langsung dari Stasiun Meteorologi Kelas I Juanda Surabaya selama tahun 2017 mewakili musim hujan, musim kemarau, dan periode transisi. Hasil penelitian menunjukkan bahwa data curah hujan produk GPM IMERG memiliki korelasi yang sangat baik untuk memperkirakan akumulasi curah hujan bulanan. Sedangkan, untuk akumulasi harian, memiliki korelasi yang sangat rendah. Sementara itu untuk akumulasi sepuluh harian, data curah hujan produk satelit GPM IMERG memiliki korelasi yang baik terutama di periode musim hujan dan musim kemarau, akan tetapi memiliki korelasi yang rendah selama periode transisi dari musim hujan ke musim kemarau atau sebaliknya. Pada umumnya, produk ini sangat bagus dalam menentukan ada atau tidaknya hujan, tetapi performanya sangat rendah dalam menentukan besarnya intensitas curah hujan.

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Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

Remote Sensing ◽

10.3390/rs12213503 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3503 ◽

Cited By ~ 1

Author(s):

Volkan Senyurek ◽

Fangni Lei ◽

Dylan Boyd ◽

Ali Cafer Gurbuz ◽

Mehmet Kurum ◽

...

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Correlation Coefficient ◽

Root Mean Square ◽

Temporal Resolution ◽

Mean Square ◽

In Situ Observations ◽

Spatio Temporal ◽

Mean Square Errors

This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within ±38° latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm−3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm−3 and 0.054 cm3 cm−3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets.

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Validation of SMAP and AMSR2 satellite soil moisture data over the Critical Zone Observatory in central Ganga plains, North India using ground-based observations

10.5194/egusphere-egu21-4667 ◽

2021 ◽

Author(s):

Saroj Dash ◽

Rajiv Sinha

Keyword(s):

Soil Moisture ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Critical Zone ◽

North India ◽

Mean Square ◽

Critical Zone Observatory ◽

Level 3

Soil moisture (SM) products derived from the passive satellite missions have been extensively used in various hydrological and environmental processes. However, validation of the satellite derived product is crucial for its reliability in several applications. In this study, we present a comprehensive validation of the descending SM product from Soil Moisture Active Passive (SMAP) Enhanced Level-3 (L3) radiometer (SMAP L3-Version 3) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) Level-3 (Version 1), over the newly established Critical Zone Observatory (CZO) within the Ganga basin, North India. The AMSR2 soil moisture product used here, has been derived using the Land Parameter Retrieval Model (LPRM) algorithm. Four SM derived products from SMAP (L-band) and AMSR2 (C1- and C2- and X-band) are validated against the in-situ observations collected from 21 SM monitoring locations distributed over the CZO within a period from September 2017 to December 2019, for a total of 62 days. Since the remotely sensed SM product has a coarser spatial resolution (here 9 km for SMAP and 10 km for AMSR2), the assessment has been carried out for the temporal variation of the measured values. Four statistical metrics such as bias, root mean square error (RMSE), unbiased root-mean-square error (ubRMSE) and the correlation coefficient (R) have been used here for the evaluation. The SMAP Level-3 products are found to show a satisfactory correlation (R>0.6) compared to the other three SM product. Both the SMAP L3 and the AMSR2 C2 SM shows a negative bias, -0.05 m3/m3 and -0.04 m3/m3 respectively whereas these values are found to be 0.04 m3/m3 and 0.06 m3/m3 for C1 and X bands of AMSR2, respectively. Furthermore, the RMSE between the SMAP L3 and in-situ data is 0.07 m3/m3, which is slightly underperformed when considering the required accuracy of SMAP. This is possibly due to variation in the sampling depth along with the sampling day distribution over CZO. The AMSR2 SM products (C1-, C2- and X-bands) are found to have a higher RMSE than SMAP L3, ranging from 0.08-0.1 m3/m3. In addition, the ubRMSE for all remotely sensed soil moisture product range from 0.06-0.08 m3/m3 with the lowest value for the SMAP L3 and AMSR2 C1. The results in this study can be used further for relevant hydrological modelling along with evaluating various downscaling strategies towards improving the coarser resolution satellite soil moisture.

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