Quantitative Estimation of Air Mass Exchange by Along-Valley Wind in the Rongbuk Valley

For a better understanding of the air mass exchange processes between the surface and free atmos-phere in the Himalayas,a Himalayan exchange between the surface and troposphere 2007 (HEST2007) campaign was carried out in the Rongbuk Valley,on the northern slope of Mt.Qomolangma,in June 2007.The wind,tem-perature and radiation conditions were measured during the campaign.Using these observation data,together with the National Centers for Environmental Prediction/the National Center for Atmospheric Research (NCEP/NCAR) reanalysis data,the air mass exchange between the inside of the valley and the outside of the valley is quantitatively estimated,based on a closed-valley method.The air mass is strongly injected into the Rongbuk Valley in the after-noon,which dominates the diurnal cycle,by a strong downward along-valley wind,with a maximum down-ward transfer rate of 9.4 cm s?1.The total air volume flux injected into the valley was 2.6×1011 m3 d?1 in 24 hours in June 2007,which is 15 times the total volume of the val-ley.The air mass transfer into the valley also exhibited a clear daily variation during the HEST2007 campaign,which can be affected by the synoptic situations through the adjustment of local radiation conditions.

Quantitative Estimation of the Changes in Soil CEC after the Removal of Organic Matter and Iron Oxides

The removal of organic matter and iron oxides could increase and decrease soil CEC in tropical and subtropical regions, but the quantitative information is insufficient so far about the change of soil CEC, the influence factors and their contribution. In this study, the subhorizon soils of 24 soil series in the tropical and subtropical China were used, pH, particle size composition, organic matter, iron oxides of these samples were measured, and also CECs were measured and compared for the original soils and after the removal of organic matter and iron oxides. The results showed that, compared with CEC of the original soil, the eliminating organic matter increased soil CEC significantly by 2.28% - 56.50% with a mean of 24.02%, but the further obliterating iron oxides decreased soil CEC significantly by 0.75% - 20.30% with a mean of 7.73%. CEC after the removal of organic matter and iron oxides had positive correlation with iron oxides (p < 0.01) and negative correlation with sand content (p < 0.01 and p < 0.05). CEC after organic matter eliminated was mainly decided by iron oxides (51.68%), followed by silt content (22.19%);while CEC after iron oxides obliterated was mainly determined by iron oxides (50.55%). The increase of CEC after organic matter eliminated was co-affected by the contents of clays, slits, iron oxides and pH (22.00% - 27.34%), while the decrease of CEC after iron oxides obliterated further was dominated by the content of organic matter (66.92%). More other soil parameters should be considered for higher predicting accuracy in the regression model of soil CEC after the removal of organic matter and iron oxides, and the recommended optimal models obtained in this study were as follows: for soil CEC after organic matter eliminated, CEC = 1.665 − 0.546pH − 0.024OM + 0.053FexOy − 0.001Silt + 0.007Clay + 0.972CECoriginal (R2 was 0.923, RSME was 1.55 cmol(+)∙kg−1, p < 0.01), while for soil CEC after iron oxides further obliterated, CEC = 1.665 − 0.546pH − 0.024OM + 0.053FexOy − 0.001Silt + 0.007Clay + 0.972CECoriginal (R2 was 0.923, RMSE was 1.55 cmol(+)∙kg−1, p < 0.01). Further research is needed in the future as for exploring internal functional mechanism in view of soil electrochemistry and mineralogy.

Radar Quantitative Precipitation Estimation Based on the Gated Recurrent Unit Neural Network and Echo-Top Data

The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.

Application of X-band Polarimetric Phased-array Radars in Quantitative Precipitation Estimation

The performance of different quantitative precipitation estimation(QPE) relationships is examined using the polarimetric variables from the X-band polarimetric phased-array radars in Guangzhou,China.Three QPE approaches,namely,R(ZH),R(ZH,ZDR) and R(KDP),are developed for horizontal reflectivity,differential reflectivity and specific phase shift rate,respectively.The estimation parameters are determined by fitting the relationships to the observed radar variables using the T-matrix method.The QPE relationships were examined using the data of four heavy precipitation events in southern China.The examination shows that the R(ZH) approach performs better for the precipitation rate less than 5 mm h-1, and R(KDP) is better for the rate higher than 5 mm h-1, while R(ZH,ZDR) has the worst performance.An adaptive approach is developed by taking the advantages of both R(ZH) and R(KDP) approaches to improve the QPE accuracy.

Operational Evaluation of the Quantitative Precipitation Estimation by a CINRAD-SA Dual Polarization Radar System

In this paper,a quantitative precipitation estimation based on the hydrometeor classification(HCA-QPE)algorithm was proposed for the first operational S band dual-polarization radar upgraded from the CINRAD/SA radar of China.The HCA-QPE algorithm,localized Colorado State University-Hydrometeor Identification of Rainfall(CSUHIDRO)algorithm,the Joint Polarization Experiment(JPOLE)algorithm,and the dynamic Z-R relationships based on variational correction QPE(DRVC-QPE)algorithm were evaluated with the rainfall events from March 1 to October 30,2017 in Guangdong Province.The results indicated that even though the HCA-QPE algorithm did not use the observed rainfall data for correction,its estimation accuracy was better than that of the DRVC-QPE algorithm when the rainfall rate was greater than 5 mm h-1;and the stronger the rainfall intensity,the greater the QPE improvement.Besides,the HCA-QPE algorithm worked better than the localized CSU-HIDRO and JPOLE algorithms.This study preliminarily evaluated the improved accuracy of QPE by a dual-polarization radar system modified from CINRAD-SA radar.

Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

Quantitative Green's function estimates for lattice quasi-periodic Schrodinger operators

In this paper,we establish quantitative Green’s function estimates for some higher-dimensional lattice quasi-periodic(QP)Schrodinger operators.The resonances in the estimates can be described via a pair of symmetric zeros of certain functions,and the estimates apply to the sub-exponential-type non-resonance conditions.As the application of quantitative Green’s function estimates,we prove both the arithmetic version of Anderson localization and the finite volume version of(1/2-)-Holder continuity of the integrated density of states(IDS)for such QP Schrodinger operators.This gives an affirmative answer to Bourgain’s problem in Bourgain(2000).

Mathematical Modeling of Landfill Gas (MSW)—Production of Gas with Methane Gas Content from Landfills (MSW)

The municipal solid waste (msw) is a source of landfill gas (msw)—with methane gas content. Preoccupations for landfill gas (msw) management date back since 1976 when, at a landfill (msw) in California (USA), it turned out practically that the landfill gas (msw) with methane gas content contains a gas with high caloric value that can be collected and used for economic purposes. The landfill gas (msw) contains methane gas (30% - 60% volume), carbon dioxide (45% - 50% volume), hydrogen sulfide and other gases. Methane gas, carbon dioxide, nitrous oxide and other gases are listed in Kyoto Protocol as high greenhouse gases. Their ecological-rational management is both a national and global preoccupation. In terms of greenhouse gases, especially methane gas, the landfill (msw) is held responsible for 3.5% - 5% of the total global greenhouse gases. Practically, the quantitative estimation of the methane gas in a municipal solid waste landfill can be done by measuring the landfill gas (msw) flow in an extraction-collection well. In Romania, a quantitative estimation relationship of methane gas from deposits (msw) was made, approaching the problem in a different way. This paper presents the calculation formula, the working algorithm, the municipal waste landfill equation and the NOMOGRAMA of a municipal solid waste landfill (msw). The NOMOGRAMA allows us to define the values for parameter -m- (number of months needed for an amount of municipal solid waste (msw) to degrade, starting with the year from which the landfill gas (msw) emission with methane gas content is calculated). Taking into account the environmental conditions for each location of municipal solid waste landfill, the calculation uses various indexes and approximations, while the fundamental parameter remains -m- defined by the NOMOGRAMA of the municipal solid waste landfill (msw). A municipal solid waste landfill (msw) is a conglomerate of waste with various biodegradation periods between 2 - 3 years and 5 - 10 - 30 years. Degradation of waste (msw) in to dissolved organic carbon will take place in a number of months defined -m- starting with the year from which the methane gas emission with the NOMOGRAMA of the municipal solid waste landfill (msw) is calculated. The -m- values for the year of the quantitative emission of methane gas can be also done analytically, which requires good experience in the ecologic-rational management of the municipal solid waste (msw).

QpefBD:A Benchmark Dataset Applied to Machine Learning for Minute-Scale Quantitative Precipitation Estimation and Forecasting

Nowcasts of strong convective precipitation and radar-based quantitative precipitation estimations have always been hot yet challenging issues in meteorological sciences.Data-driven machine learning,especially deep learning,provides a new technical approach for the quantitative estimation and forecasting of precipitation.A high-quality,large-sample,and labeled training dataset is critical for the successful application of machine-learning technology to a specific field.The present study develops a benchmark dataset that can be applied to machine learning for minutescale quantitative precipitation estimation and forecasting(QpefBD),containing 231,978 samples of 3185 heavy precipitation events that occurred in 6 provinces of central and eastern China from April to October 2016-2018.Each individual sample consists of 8 products of weather radars at 6-min intervals within the time window of the corresponding event and products of 27 physical quantities at hourly intervals that describe the atmospheric dynamic and thermodynamic conditions.Two data labels,i.e.,ground precipitation intensity and areal coverage of heavy precipitation at 6-min intervals,are also included.The present study describes the basic components of the dataset and data processing and provides metrics for the evaluation of model performance on precipitation estimation and forecasting.Based on these evaluation metrics,some simple and commonly used methods are applied to evaluate precipitation estimates and forecasts.The results can serve as the benchmark reference for the performance evaluation of machine learning models using this dataset.This paper also gives some suggestions and scenarios of the QpefBD application.We believe that the application of this benchmark dataset will promote interdisciplinary collaboration between meteorological sciences and artificial intelligence sciences,providing a new way for the identification and forecast of heavy precipitation.

A CRPS-Based Spatial Technique for the Verification of Ensemble Precipitation Forecasts

Traditional precipitation skill scores are affected by the well-known"double penalty"problem caused by the slight spatial or temporal mismatches between forecasts and observations.The fuzzy(neighborhood)method has been proposed for deterministic simulations and shown some ability to solve this problem.The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts.We developed an ensemble precipitation verification skill score,i.e.,the Spatial Continuous Ranked Probability Score(SCRPS),and used it to extend spatial verification from deterministic into ensemble forecasts.The SCRPS is a spatial technique based on the Continuous Ranked Probability Score(CRPS)and the fuzzy method.A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency,which were then used in the reference score to calculate the skill score of the SCRPS.The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained.The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used.

Quantitative analysis of CO_(2) embodiment in international trade:An overview of emerging literatures

The increasing volume of CO_(2) embodiment in international trade adds a layer of complexity to environmental policies and has raised arguments on the traditional production based responsibility for CO_(2) emissions.In order to help understand the quantity of CO_(2) embodiment in trade and its policy implications,this paper gives observations to recently emerging literatures that quantitatively discuss CO_(2) embodiment in trade.The analytical approaches share the principle of using input and output modeling but vary dramatically in study boundary and estimation accuracy.The calculations can be roughly categorized into three types:direct quantification of CO_(2) embodiments in multiregional trade,direct quantification of CO_(2) embodiment in bilateral trade,and indirect analysis by comparing the scenarios with or without trade.The practical estimations strongly rely on trade partner selection and data availability.An obvious imbalance of net CO_(2) embodiment in the commodity trade between major developed countries and developing economies as a whole was confirmed by these literatures.Carbon taxes and other possible limitations on CO_(2) emissions have been addressed.The consistency across the calculations could be enhanced by systematic analyses in more detail to convince the international community to take binding commitments for the reduction of global CO_(2) emissions.

Global existence of weak solutions to the drift-flux system for general pressure laws

The initial value problem of the multi-dimensional drift-flux model for two-phase flow is investigated in this paper,and the global existence of weak solutions with finite energy is established for general pressure-density functions without the monotonicity assumption.

Vector-valued operators, optimal weighted estimates and the C_p condition

In this paper some new results concerning the C_p classes introduced by Muckenhoupt(1981)and later extended by Sawyer(1983),are provided.In particular,we extend the result to the full expected range p>0,to the weak norm,to other operators and to their vector-valued extensions.Some of those results rely upon sparse domination,which in the vector-valued case are provided as well.We will also provide sharp weighted estimates for vector-valued extensions relying on those sparse domination results.

Variations of Raindrop Size Distribution and Radar Retrieval in Outer Rainbands of Typhoon Mangkhut(2018)

The evolution of the microphysical properties of raindrops from Typhoon Mangkhut’s outer rainbands as the storm made landfall in South China in September 2018 was investigated.The observations by three two-dimensional video disdrometers deployed in central Guangdong Province were analyzed concurrently.It was found that the radial distribution of the median volume diameter(D_(0))and normalized intercept parameter(N_(w))varied in different stages,and that raindrops smaller than 3.0 mm contributed more than 99%of the total precipitation.Considering the characteristics of precipitation in the typhoon outer rainband,a modified stratiform rain(SR)-convective rain(CR)separator line is proposed based on D_(0) and N_(w) scatterplots.Meanwhile,an“S-C likelihood index”is introduced,which was used to classify three rain types(SR,CR,and mixed rain).The CR results were highly consistent with those of the improved typhoon precipitation classification method based on rain rate.By calculating effectively the radar reflectivity factor(Ze)in the Ku and Ka bands,D0-Ze and N_(w)-D_(0) empirical relations were thereby derived for improving the accuracy of rainfall retrieval.Among the four quantitative precipitation estimators using S-band dual-polarimetric radar parameters simulated by the T-matrix method,the estimator that adopted the specific differential phase and differential reflectivity was found to be the most effective for both SR and CR.