A Storm Surge Intensity Classification Based on Extreme Water Level and Concomitant Wave Height

Storm surge is one of the predominant natural threats to coastal communities. Qingdao is located on the southern coast of the Shandong Peninsula in China. The storm surge disaster in Qingdao depends on various influencing factors such as the intensity, duration, and route of the passing typhoon, and thus a comprehensive understanding of natural coastal hazards is essential. In order to make up the defects of merely using the warning water level, this paper presents two statistical distribution models(Poisson Bi- variable Gumbel Logistic Distribution and Poisson Bi-variable Log-normal Distribution) to classify the intensity of storm surge. We emphasize the joint return period of typhoon-induced water levels and wave heights measured in the coastal area of Qingdao since 1949. The present study establishes a new criterion to classify the intensity grade of catastrophic storms using the typhoon surge estimated by the two models. A case study demonstrates that the new criterion is well defined in terms of probability concept, is easy to implement, and fits well the calculation of storm surge intensity. The procedures with the proposed statistical models would be useful for the disaster mitigation in other coastal areas influenced by typhoons.

Wave parameters retrieval for dual-polarization C-band synthetic aperture radar using a theoretical-based algorithm under cyclonic conditions

Theoretical-based ocean wave retrieval algorithms are applied by inverting a synthetic aperture radar(SAR)intensity spectrum into a wave spectrum, that has been developed based on a SAR wave mapping mechanism. In our previous studies, it was shown that the wave retrieval algorithm, named the parameterized first-guess spectrum method(PFSM), works for C-band and X-band SAR at low to moderate sea states. In this work, we investigate the performance of the PFSM algorithm when it is applied for dual-polarization c-band sentinel-1(S-1) SAR acquired in extra wide-swath(EW) and interferometric wide-swath(IW) mode under cyclonic conditions.Strong winds are retrieved from six vertical-horizontal(VH) polarization S-1 SAR images using the c-band crosspolarization coupled-parameters ocean(C-3 PO) model and then wave parameters are obtained from the image at the vertical-vertical(VV) polarization channel. significant wave height(SWH) and mean wave period(MWP) are compared with simulations from the WAVEWATCH-III(WW3) model. The validation shows a 0.69 m root mean square error(RMSE) of SWH with a –0.01 m bias and a 0.62 s RMSE of MWP with a –0.17 s bias. Although the PFSM algorithm relies on a good quality SAR spectrum, this study confirms the applicability for wave retrieval from an S-1 SAR image. Moreover, it is found that the retrieved results have less accuracy on the right sector of cyclone eyes where swell directly affects strong wind-sea, while the PFSM algorithm works well on the left and rear sectors of cyclone eyes where the interaction of wind-sea and swell is relatively poor.

Development and validation of an ocean wave retrieval algorithm for VV-polarization Sentinel-1 SAR data

The purpose is to study the accuracy of ocean wave parameters retrieved from C-band VV-polarization Sentinel-1Synthetic Aperture Radar（SAR） images, including both significant wave height（SWH） and mean wave period（MWP）, which are both calculated from a SAR-derived wave spectrum. The wind direction from in situ buoys is used and then the wind speed is retrieved by using a new C-band geophysical model function（GMF） model,denoted as C-SARMOD. Continuously, an algorithm parameterized first-guess spectra method（PFSM） is employed to retrieve the SWH and the MWP by using the SAR-derived wind speed. Forty-five VV-polarization Sentinel-1 SAR images are collected, which cover the in situ buoys around US coastal waters. A total of 52 subscenes are selected from those images. The retrieval results are compared with the measurements from in situ buoys. The comparison performs good for a wind retrieval, showing a 1.6 m/s standard deviation（STD） of the wind speed, while a 0.54 m STD of the SWH and a 2.14 s STD of the MWP are exhibited with an acceptable error.Additional 50 images taken in China＇s seas were also implemented by using the algorithm PFSM, showing a 0.67 m STD of the SWH and a 2.21 s STD of the MWP compared with European Centre for Medium-range Weather Forecasts（ECMWF） reanalysis grids wave data. The results indicate that the algorithm PFSM works for the wave retrieval from VV-polarization Sentinel-1 SAR image through SAR-derived wind speed by using the new GMF C-SARMOD.

MODIS captures large-scale atmospheric gravity waves over the Atlantic Ocean

On April 27,2016,a striking true-color satellite image acquired by the Moderate Resolution Imaging Spectroradiometer（MODIS）onboard National Aeronautics and Space Administration’s（NASA’s）Aqua satellite showed several groups of very well structured arc cloud patterns（Fig.1）,which are associaed with atmospheric gravity waves,aligned in the middle of the Atlantic Ocean between

The first Sentinel-1 SAR image of a typhoon

In this note, we present the first Sentinel-1 synthetic aperture radar （SAR） typhoon image acquired in the northwest Pacific on October 4, 2014. The eye shape and sea surface wind patterns associated with Typhoon Phanfone are clearly shown in the high-quality SAR image. SAR winds retrieval procedure was given but the actual wind estimates will only be available after the European Space Agency （ESA） releases the official calibration coefficients in order to accurately derive the SAR-measured normalized radar cross section. This study demonstrates the advantage of Sentinel- 1 SAR with regards to imaging fine scale typhoon patterns on the sea surface beneath storm clouds. This paper also advocates the use of Sentinel-1 SAR data that is made freely and openly available worldwide for the first time in civilian SAR history.

Temporal variability of atmospheric particulate matter and chemical composition during a growing season at an agricultural site in northeastern China

This study presents the observations of PM10 and PM2.5 concentrations at an agricultural site from April to October 2012 in Dehui city,China.Ambient air was sampled by filter-based samplers and online PM monitors.The filter samples were analyzed to determine the abundance of ionic/inorganic elements,organic carbon（OC） and elemental carbon（EC）.The daily PM10 concentrations varied significantly over the monitoring period,with an average of168 ± 63（in the range of 52-277） μg/m^3 during the land preparation/planting period（26 April-15 June）,85 ± 65（36-228） μg/m^3 during the growing season（16 June-25 September）,and 207 ±88（103-310） μg/m^3 during the harvest period（26 September-31 October）.PM2.5 accounted for44%,56%and 66%of atmospheric PM10 during these periods,respectively.The PM10 diurnal variation showed a distinct peak from 16：00 to 21：00（LST） during the growing and harvesting seasons,while a gradual increase throughout the daytime until 17：00 was observed during tilling season.Mineral dust elements（Al,Ca,Fe,and Mg） dominated the PM10 chemical composition during the tilling season;OC,NO3^-,SO4^（2-） and NH4~＋ during the growing season;and carbonaceous species（i.e.,OC and EC） during the harvesting season.Our results indicate that the soil particles emitted by farm tillage and organic matter released from straw burning are the two most significant sources of PM10 emissions contributing to the recurring high pollution events in this region.Therefore,development of agricultural PM inventories from soil tillage and straw burning is prioritized to support air quality modeling.

Study of Vertical Breakwater Reliability Based on Copulas

The reliability of a vertical breakwater is calculated using direct integration methods based on joint density functions.The horizontal and uplifting wave forces on the vertical breakwater can be well fitted by the lognormal and the Gumbel distributions,respectively.The joint distribution of the horizontal and uplifting wave forces is analyzed using different probabilistic distributions,including the bivariate logistic Gumbel distribution,the bivariate lognormal distribution,and three bivariate Archimedean copulas functions constructed with different marginal distributions simultaneously.We use the fully nested copulas to construct multivariate distributions taking into account related variables.Different goodness fitting tests are carried out to determine the best bivariate copula model for wave forces on a vertical breakwater.We show that a bivariate model constructed by Frank copula gives the best reliability analysis,using marginal distributions of Gumbel and lognormal to account for uplifting pressure and horizontal wave force on a vertical breakwater,respectively.The results show that failure probability of the vertical breakwater calculated by multivariate density function is comparable to those by the Joint Committee on Structural Safety methods.As copulas are suitable for constructing a bivariate or multivariate joint distribution,they have great potential in reliability analysis for other coastal structures.

美国西部的长期干旱变化

一些水文气候资料分析表明,美国西部正在经历着多年的严重干旱。然而,利用重建的覆盖美国西部大部分地区的过去1200年的网格化干旱资料进行分析,看出与更早时期出现的极端干旱和发生在公元800～1300年间(中世纪暖期(MWP))的大范围严重干旱相比,现在正经历的干旱还不算很严重。如果美国西部干旱程度的加强是一种对气候变暖的自然响应,那么任何将来温度增暖的趋势都将会加剧美国西部地区的长期干旱。

Monthly Forecast of Indian Southwest Monsoon Rainfall Based on NCEP’s Coupled Forecast System

The monthly forecast of Indian monsoon rainfall during June to September is investigated by using the hindcast data sets of the National Centre for Environmental Prediction (NCEP)’s operational coupled model (known as the Climate Forecast System) for 25 years from 1981 to 2005 with 15 ensemble members each. The ensemble mean monthly rainfall over land region of India from CFS with one month lead forecast is underestimated during June to September. With respect to the inter-annual variability of monthly rainfall it is seen that the only significant correlation coefficients (CCs) are found to be for June forecast with May initial condition and September rainfall with August initial conditions. The CFS has got lowest skill for the month of August followed by that of July. Considering the lower skill of monthly forecast based on the ensemble mean, all 15 ensemble members are used separately for the preparation of probability forecast and different probability scores like Brier Score (BS), Brier Skill Score (BSS), Accuracy, Probability of Detection (POD), False Alarm Ratio (FAR), Threat Score (TS) and Heidke Skill Score (HSS) for all the three categories of forecasts (above normal, below normal and normal) have been calculated. In terms of the BS and BSS the skill of the monthly probability forecast in all the three categories are better than the climatology forecasts with positive BSS values except in case of normal forecast of June and July. The “TS”, “HSS” and other scores also provide useful probability forecast in case of CFS except the normal category of July forecast. Thus, it is seen that the monthly probability forecast based on NCEP CFS coupled model during the southwest monsoon season is very encouraging and is found to be very useful.

Regional and Global Land Data Assimilation Systems: Innovations,Challenges, and Prospects

Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectbased, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications(from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure,parameters, and forcing are discussed.

Assessment of VIIRS 375 m active fire using tropical peatland combustion algorithm applied to Landsat-8 over Indonesia’s peatlands

VIIRS 375 m active fire data(VNP14IMG),the highest spatial resolution available cost-free fire product,were assessed for representing fire in typical degraded tropical peatlands in Indonesia.The results of applying the Tropical Peatland Combustion Algorithm to Landsat-8(ToPeCAl-L8)daytime imagery were utilised as the fire references.To permit the comparison of non-simultaneous VNP14IMG and ToPeCAl-L8,peatland fire propagation speeds resulting from previous study using TET-1 data in Central Kalimantan’s peatlands were utilised.Most peatland fires were still within 750 m from their source over 15 h under uniform conditions,except for very large fires.The detection rates of nighttime VNP14IMG compared with ToPeCAl-L8 showed about 80%agreement for small fire areas(<14 ha).For fires larger than 14 ha,a dissolved 375 m buffer(cluster buffer)of VNP14IMG active fires with an integration of nighttime and daytime acquisitions,produced a probability of detection up to 90%.These results generated a recommendation for implementing cluster buffer analysis and integration of nighttime and daytime analysis of VNP14IMG data for better accuracy in fire detection for ground fire management.They also demonstrate the utility of the ToPeCAl-L8 algorithm with VIIRS 375 m active fire data.

Challenges of modeling solar disturbances' arrival times at the Earth

In recent years remarkable advances have been made in the development of phys-ics based models of various parts of the solar-terrestrial system (see JASTP special issues, October, November 2004; February 2007). In this paper, we focus our dis-cussions in a specific region of the Sun to the Earth’s environment (i.e. 1 AU). It is well-known that geomagnetic storms are caused by solar eruptions. The conse-quences of these storms include particle acceleration, solar wind impact on the Earth’s magnetosphere and ionosphere, UV-EUV radiation effects on the lower at-mosphere, etc. One of the main challenges is to predict the arrival time at 1 AU of the solar disturbance. The prospects look good for an accurate, real-time forecast scheme built on the acquisition of solar, heliosphere and the near-Earth data and large-scale models. However, the accuracy of these models still needs improve-ment. We will discuss the present status of the models and challenges to improve the simulation models.