The Impact of Horizontal Resolution on the CNOP and on Its Identified Sensitive Areas for Tropical Cyclone Predictions

In this study, the ilnpacts of horizontal resolution on the conditional nonlinear optimal perturbation （CNOP） and on its identified sensitive areas were investigated for tropical cyclone predictions. Three resolutions, 30 km, 60 km, and 120 kin, were studied for three tropical cyclones, TC Mindulle （2004）, TC Meari （2004）, and TC Matsa （2005）. Results show that CNOP may present different structures with different resolutions, and the major parts of CNOP become increasingly localized with increased horizontal resolution. CNOP produces spiral and baroclinic structures, which partially account for its rapid amplification. The differences in CNOP structures result in different sensitive areas, but there are common areas for the CNOP-identified sensitive areas at various resolutions, and the size of the common areas is different from case to case. Generally, the forecasts benefit more from the reduction of the initial errors in the sensitive areas identified using higher resolutions than those using lower resolutions. However, the largest improvement of the forecast can be obtained at the resolution that is not the highest for some cases. In addition, the sensitive areas identified at lower resolutions are also helpful for improving the forecast with a finer resolution, but the sensitive areas identified at the same resolution as the forecast would be the most beneficial.

The Time and Regime Dependencies of Sensitive Areas for Tropical Cyclone Prediction Using the CNOP Method

This study examines the time and regime dependencies of sensitive areas identified by the conditional nonlinear optiflml perturbation （CNOP） method for forecasts of two typhoons. Typhoon Meari （2004） was weakly nonlinear and is herein referred to as the linear case, while Typhoon Matsa （2005） was strongly nonlinear and is herein referred to as the nonlinear case. In the linear case, the sensitive areas identified for special forecast times when the initial time was fixed resembled those identified for other forecast times. Targeted observations deployed to improve a special time forecast would thus also benefit forecasts at other times. In the nonlinear case, the similarities among the sensitive areas identified for different forecast times were more limited. The deployment of targeted observations in the nonlinear case would therefore need to be adapted to achieve large improvements for different targeted forecasts. For both cases, the closer the forecast time, the higher the similarities of the sensitive areas. When the forecast time was fixed, the sensitive areas in the linear case diverged continuously from the verification area as the forecast period lengthened, while those in the nonlinear case were always located around the initial cyclones. The deployment of targeted observations to improve a special forecast depends strongly on the time of deployment. An examination of the efficiency gained by reducing initial errors within the identified sensitive areas confirmed these results. In general, the greatest improvement in a special time forecast was obtained by identifying the sensitive areas for the corresponding forecast time period.

Identifying sensitive areas of adaptive observations for prediction of the Kuroshio large meander using a shallow-water model

Sensitive areas for prediction of the Kuroshio large meander using a 1.5-layer,shallowwater ocean model were investigated using the conditional nonlinear optimal perturbation(CNOP) and first singular vector(FSV) methods.A series of sensitivity experiments were designed to test the sensitivity of sensitive areas within the numerical model.The following results were obtained:(1) the effect of initial CNOP and FSV patterns in their sensitive areas is greater than that of the same patterns in randomly selected areas,with the effect of the initial CNOP patterns in CNOP sensitive areas being the greatest;(2) both CNOP- and FSV-type initial errors grow more quickly than random errors;(3) the effect of random errors superimposed on the sensitive areas is greater than that of random errors introduced into randomly selected areas,and initial errors in the CNOP sensitive areas have greater effects on final forecasts.These results reveal that the sensitive areas determined using the CNOP are more sensitive than those of FSV and other randomly selected areas.In addition,ideal hindcasting experiments were conducted to examine the validity of the sensitive areas.The results indicate that reduction(or elimination) of CNOP-type errors in CNOP sensitive areas at the initial time has a greater forecast benefit than the reduction(or elimination) of FSVtype errors in FSV sensitive areas.These results suggest that the CNOP method is suitable for determining sensitive areas in the prediction of the Kuroshio large-meander path.

The Impact of Moist Physics on the Sensitive Area Identification for Heavy Rainfall Associated Weather Systems

The impact of moist physics on the sensitive areas identified by conditional nonlinear optimal perturbation(CNOP)is examined based on four typical heavy rainfall cases in northern China through performing numerical experiments with and without moist physics.Results show that the CNOP with moist physics identifies sensitive areas corresponding to both the lower-(850−700 hPa)and upper-level(300−100 hPa)weather systems,while the CNOP without moist physics fails to capture the sensitive areas at lower levels.The reasons for the CNOP peaking at different levels can be explained in both algorithm and physics aspects.Firstly,the gradient of the cost function with respect to initial perturbations peaks at the upper level without moist physics which results in the upper-level peak of the CNOP,while it peaks at both the upper and lower levels with moist physics which results in both the upper-and lower-level peaks of the CNOP.Secondly,the upper-level sensitive area is associated with high baroclinicity,and these dynamic features can be captured by both CNOPs with and without moist physics.The lower-level sensitive area is associated with moist processes,and this thermodynamic feature can be captured only by the CNOP with moist physics.This result demonstrates the important contribution of the initial error of lower-level systems that are related to water vapor transportation to the forecast error of heavy rainfall associated weather systems,which could be an important reference for heavy rainfall observation targeting.

Exploring sensitive area in the tropical Indian Ocean for El Niño prediction: implication for targeted observation

Based on initial errors of sea temperature in the tropical Indian Ocean that are most likely to induce spring predictability barrier(SPB)for the El Niño prediction,the sensitive area of sea temperature in the tropical Indian Ocean for El Niño prediction starting from January is identified using the CESM1.0.3(Community Earth System Model),a fully coupled global climate model.The sensitive area locates mainly in the subsurface of eastern Indian Ocean.The effectiveness of applying targeted observation in the sensitive area is also evaluated in an attempt to improve the El Niño prediction skill.The results of sensitivity experiments indicate that if initial errors exist only in the tropical Indian Ocean,applying targeted observation in the sensitive area in the Indian Ocean can significantly improve the El Niño prediction.In particular,for SPB-related El Niño events,when initial errors of sea temperature exist both in the tropical Indian Ocean and the Pacific Ocean,which is much closer to the realistic predictions,if targeted observations are conducted in the sensitive area of tropical Pacific,the prediction skills of SPB-related El Niño events can be improved by 20.3%in general.Moreover,if targeted observations are conducted in the sensitive area of tropical Indian Ocean in addition,the improvement of prediction skill can be increased by 25.2%.Considering the volume of sensitive area in the tropical Indian Ocean is about 1/3 of that in the tropical Pacific Ocean,the prediction skill improvement per cubic kilometer in the sensitive area of tropical Indian Ocean is competitive to that of the tropical Pacific Ocean.Additional to the sensitive area of the tropical Pacific Ocean,sensitive area of the tropical Indian Ocean is also a very effective and cost-saving area for the application of targeted observations to improve El Niño forecast skills.

Identifying the sensitive areas in targeted observation for predicting the Kuroshio large meander path in a regional ocean model

With the Regional Ocean Modeling System(ROMS),this paper investigates the sensitive areas in targeted observation for predicting the Kuroshio large meander(LM)path using the conditional nonlinear optimal perturbation approach.To identify the sensitive areas,the optimal initial errors(OIEs)featuring the largest nonlinear evolution in the LM prediction are first calculated;the resulting OIEs are localized mainly in the upper 2500 m over the LM upstream region,and their spatial structure has certain similarities with that of the optimal triggering perturbation.Based on this spatial structure,the sensitive areas are successfully identified,located southeast of Kyushu in the region(29°–32°N,131°–134°E).A series of sensitivity experiments indicate that both the positions and the spatial structure of initial errors have important effects on the LM prediction,verifying the validity of the sensitive areas.Then,the effect of targeted observation in the sensitive areas is evaluated through observing system simulation experiments.When targeted observation is implemented in the identified sensitive areas,the prediction errors are effectively reduced,and the prediction skill of the LM event is improved significantly.This provides scientific guidance for ocean observations related to enhancing the prediction skill of the LM event.

Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea

The sensitive area of targeted observations for short-term(7 d)prediction of vertical thermal structure(VTS)in summer in the Yellow Sea was investigated.We applied the Conditional Nonlinear Optimal Perturbation(CNOP)method and an adjoint-free algorithm with the Regional Ocean Modeling System(ROMS).We used vertical integration of CNOP-type temperature errors to locate the sensitive areas,where reduction of initial errors is expected to yield the greatest improvement in VTS prediction for the selected verification area.The identified sensitive areas were northeast−southwest orientated northeast to the verification area,which were possibly related to the southwestward background currents.Then,we performed a series of sensitivity experiments to evaluate the effectiveness of the identified sensitive areas.Results show that initial errors in the identified sensitive areas had the greatest negative effect on VTS prediction in the verification area compared to errors in other areas(e.g.,the verification area and areas to its east and northeast).Moreover,removal of initial errors through deploying simulated observations in the identified sensitive areas led to more refined prediction than correction of initial conditions in the verification area itself.Our results suggest that implementation of targeted observation in the CNOP-based sensitive areas is an effective method to improve short-term prediction of VTS in summer in the Yellow Sea.

Preliminary Study of Sensitive Areas for Several Tropical Cyclone Track Prediction Cases in 2007

Conditional （CNOP） obtained by nonlinear optimal perturbation the ensemble-based calculation method is employed to find possible sensitive areas for improving 48-h or more than 48-h tropical cyclone （TC） track predictions in several cases affecting China in 2007. These sensitive areas are examined by observing system simulation experiments （OSSEs）. Results show that these sensitive areas improve TC track predictions for 48 h or more to different extents. Further analysis is performed to determine the distribution characteristics of sensitive areas in these cases. Results show that areas south of Luzon and over surrounding oceans are significant for 48-h or more than 48-h TC track predictions, especially 60-h to 72-h track predictions. Areas over oceans north or east to Taiwan Island seem to be secondary sensitive for 48-h or more than 48-h TC track predictions.

Study on Connection of Spatial Planning of Oil and Gas Infrastructure and Environmental Sensitive Area

By analyzing the influence of pollution factors in each process on the environmentally sensitive area in construction and operation of oil and gas infrastructure,main problems were obtained:delimitation and implementation of three control lines in land space planning,relevant environmental protection laws and regulations not perfect and specific,delimitation of environmentally sensitive area lack of sufficient demonstration,"conditional permission to pass"not be raised to an explicit provision,urban energy corridor planning not fully implement the concept of environmental protection,and the idea of adjacent spatial planning not be implemented yet.Moreover,it put forward countermeasures and suggestions for the construction department of oil and gas infrastructure.

Identifying the sensitive area in adaptive observation for predicting the upstream Kuroshio transport variation in a 3-D ocean model

Using the conditional nonlinear optimal perturbation(CNOP) approach, sensitive areas of adaptive observation for predicting the seasonal reduction of the upstream Kuroshio transport(UKT) were investigated in the Regional Ocean Modeling System(ROMS). The vertically integrated energy scheme was utilized to identify sensitive areas based on two factors: the specific energy scheme and sensitive area size. Totally 27 sensitive areas, characterized by three energy schemes and nine sensitive area sizes, were evaluated. The results show that the total energy(TE) scheme was the most effective because it includes both the kinetic and potential components of CNOP. Generally, larger sensitive areas led to better predictions. The size of 0.5% of the model domain was chosen after balancing the effectiveness and efficiency of adaptive observation. The optimal sensitive area OSen was determined accordingly. Sensitivity experiments on OSen were then conducted, and the following results were obtained:(1) In OSen, initial errors with CNOP or CNOP-like patterns were more likely to yield worse predictions, and the CNOP pattern was the most unstable.(2) Initial errors in OSen rather than in other regions tended to cause larger prediction errors. Therefore, adaptive observation in OSen can be more beneficial for predicting the seasonal reduction of UKT.

Understanding the spatial distribution of hydrologic sensitive areas in the landscape using soil topographic index approach

Maintaining healthy watershed is pivotal to ensure sustainability in water resources thereby improving the carrying capacity of the earth.Understanding and identifying the spatial variability of hydrologically sensitive areas(HSAs)in a watershed is an important step to prioritizing the landscape to maintain water sustainability with limited resources.A spatial technique known as Soil Topographic Index(STI)was used to identify HSAs in the landscape.This study was conducted in Clinton and Tewksbury Townships in New Jersey,United States.Three different scenarios(STI>=9,STI>=10,and STI>=11)were conducted to understand the spatial distribution of HSAs in the watershed.The following conclusions were derived from this study.Firstly,a more detail representation of HSAs in the watershed was observed when applying the STI technique with a fine scale light detection and ranging(LiDAR)digitial elevation model.Secondly,all three scenarios consistently identified perennial stream corridors as HSAs;therefore,it is important to protect perennial stream corridors through implementation of various land use controls.Thirdly,this study analyzes the land use pattern of HSAs under the three scenarios and identifies the HSAs for high intensity land uses such as agriculture and urban to be the high priority locations for implementing best management practices for water quality improvements.The procedures developed in this study can be applied to watersheds in other parts of the world with similar physiographic characteristics.

The Impact of Verification Area Design on Tropical Cyclone Targeted Observations Based on the CNOP Method

This study investigated the impact of different verification-area designs on the sensitive areas identified using the conditional nonlinear optimal perturbation （CNOP） method for tropical cyclone targeted observations.The sensitive areas identified using the first singular vector （FSV） method,which is the linear approximation of CNOP,were also investigated for comparison.By analyzing the validity of the sensitive areas,the proper design of a verification area was developed.Tropical cyclone Rananim,which occurred in August 2004 in the northwest Pacific Ocean,was studied.Two sets of verification areas were designed;one changed position,and the other changed both size and position.The CNOP and its identified sensitive areas were found to be less sensitive to small variations of the verification areas than those of the FSV and its sensitive areas.With larger variations of the verification area,the CNOP and the FSV as well as their identified sensitive areas changed substantially.In terms of reducing forecast errors in the verification area,the CNOP-identified sensitive areas were more beneficial than those identified using FSV.The design of the verification area is important for cyclone prediction.The verification area should be designed with a proper size according to the possible locations of the cyclone obtained from the ensemble forecast results.In addition,the development trend of the cyclone analyzed from its dynamic mechanisms was another reference.When the general position of the verification area was determined,a small variation in size or position had little influence on the results of CNOP.

Study on the Environmental Impact of Oil and Gas Field Development on the Ecological Red Line Area

To cooperate with the five ministries and commissions of the state to carry out joint investigation on the environmentally sensitive areas involved in oil and gas exploration and development,for the problems found in survey,containing complex type and numerous amount of ecologically sensitive space and ecological red line involved in oil and gas field enterprises,scientific nature of delimitation,lack of strong support of laws and regulations for forced withdrawal of oil and gas production facilities in these areas,some countermeasures and suggestions were proposed,such as further evaluating and combing scope and functional zoning of existing environmentally sensitive areas and ecological red lines,treating differently,enhancing pertinence of prohibition in ecologically sensitive regions,declining blindness of the withdrawal of oil and gas facilities in environmentally sensitive areas,strengthening seriousness of approval of exploration and mining rights of oil and gas resources,and establishing strategic reserve exploration and hierarchical development mechanism. Moreover,oil and gas field enterprises should integrate more efforts to ① accelerate to find out the current situation of environmental quality,② adhere to developing in protection,and protecting in development,③ increase attention and participation strengthen of providing technical support for national oil and gas exploration and development strategy evaluation,④ accelerate communication and docking with local governments on the ecological red line,⑤ actively strive to be included in the positive list management of local governments,⑥ accelerate to establish and perfect primary database of oil and gas production and facilities construction,and ⑦ document management information system of the ecological red line.

The Roles of Spatial Locations and Patterns of Initial Errors in the Uncertainties of Tropical Cyclone Forecasts

In this study, a series of sensitivity experiments were performed for two tropical cyclones （TCs）, TC Longwang （2005） and TC Sinlaku （2008）, to explore the roles of locations and patterns of initial errors in uncertainties of TC forecasts. Specifically, three types of initial errors were generated and three types of sensitive areas were determined using conditional nonlinear optimal perturbation （CNOP）, first singular vector （FSV）, and composite singular vector （CSV） methods. Additionally, random initial errors in randomly selected areas were considered. Based on these four types of initial errors and areas, we designed and performed 16 experiments to investigate the impacts of locations and patterns of initial errors on the nonlinear developments of the errors, and to determine which type of initial errors and areas has the greatest impact on TC forecasts. Overall, results from the experiments indicate the following： （1） The impact of random errors introduced into the sensitive areas was greater than that of errors themselves fixed in the randomly selected areas. From the perspective of statisticul analysis, and by comparison, the impact of random errors introduced into the CNOP target area was greatest. （2） The initial errors with CNOP, CSV, or FSV patterns were likely to grow faster than random errors. （3） The initial errors with CNOP patterns in the CNOP target areas had the greatest impacts on the final verification forecasts.

The Spatial Patterns of Initial Errors Related to the “Winter Predictability Barrier” of the Indian Ocean Dipole

In this study, using the Geophysical Fluid Dynamics Laboratory Climate Model version 2pl （GFDL CM2pl） coupled model, the winter predictability barrier （WPB） is found to exist in the model not only in the growing phase but also the Indian Ocean dipole （IOD） decaying phase of positive events due to the effect of initial errors. In particular, the WPB is stronger in the growing phase than in the decaying phase. These results indicate that initial errors can cause the WPB. The domi- nant patterns of the initial errors that cause the occurrence of the WPB often present an eastern-western dipole both in the surface and subsurface temperature components. These initial errors tend to concentrate in a few areas, and these areas may represent the sensitive areas of the predictions of positive IOD events. By increasing observations over these areas and eliminating initial errors here, the WPB phenomenon may be largely weakened and the forecast skill greatly improved.

INSTABILITY OF SYMMETRIC TYPHOON CIRCULATION AND ADAPTIVE OBSERVATION

This study presents a new way to identify the sensitive areas,which are determined by invoking the negative anomalies of moist potential vorticity (MPV) for typhoon adaptive observations. It is found that the areas of negative MPV are the symmetric instability areas and can be taken as sensitive areas for typhoon adaptive observations. Three typhoons in 2008,Nuri,Fung-wong,and Fengshen,were simulated with the help of MM5 model. It is shown that these typhoons are well simulated in the first 12 hours. Based on these investigations,the calculations of MPV are carried out sequentially. The result shows that the negative maxima of MPV are always around the typhoon eyes for all the cases,which means that the sensitive areas are also near them all the time.

A GIS-Based Evaluation of Environmental Sensitivity for an Urban Expressway in Shenzhen, China

Urban eco-environmental degradation is becoming inevitable due to the extensive urbanization, popula- tion growth, and socioeconomic development in China. One of the traffic arteries in Shenzhen is an urban expressway that is under construction and that runs across environmentally sensitive areas （ESAs）. The environmental pollution from urban expressways is critical, due to the characteristics of expressways such as high runoff coefficients, considerable contaminant accumulation, and complex pollutant ingredi- ents. ESAs are vulnerable to anthropogenic disturbances and hence should be given special attention. In order to evaluate the environmental sensitivity along this urban expressway and minimize the influences of the ongoing road construction and future operation on the surrounding ecosystem, the environmental sensitivity of the relevant area was evaluated based on the application of a geographic information sys- tem （GIS）. A final ESA map was classified into four environmental sensitivity levels; this classification indicates that a large proportion of the expressway passes through areas of high sensitivity, representing 11.93 km or 52.3% of the total expressway, and more than 90% of the total expressway passes through ESAs. This study provides beneficial information for optimal layout schemes of initial rainfall runofftreatment facilities developed from low-impact development （LID） techniques in order to minimize the impact of polluted road runoff on the surrounding ecological environment.

Land degradation sensitivity assessment and convergence analysis in Korla of Xinjiang, China

Land degradation has a major impact on environmental and socio-economic sustainability. Scientific methods are necessary to monitor the risk of land degradation. In this study, the environmental sensitive area index(ESAI) was utilized to assess land degradation sensitivity and convergence analysis in Korla, a typical oasis city in Xinjiang of China, which is located on the northeast border of the Tarim Basin. A total of 18 indicators depicting soil, climate, vegetation, and management qualities were used to illustrate spatial-temporal patterns of land degradation sensitivity from 1994 to 2018. We investigated the causes of spatial convergence and divergence based on the Ordinary Least Squares(OLS) and Geographically Weighted Regression(GWR) models. The results show that the branch of the Tianshan Mountains and oasis plain had a low sensitivity to land degradation, while the Tarim Basin had a high risk of land degradation. More than two-thirds of the study area can be categorized as "critical" sensitivity classes. The largest percentage(32.6%) of fragile classes was observed for 2006. There was no significant change in insensitive or low-sensitivity areas, which accounted for less than 0.4% of the entire observation period. The ESAI of the four time periods(1994–1998, 1998–2006, 2006–2010, and 2010–2018) formed a series of convergence patterns. The convergence patterns of 1994–1998 and 1998–2006 can be explained by the government's efforts to "Returning Farmland to Forests" and other governance projects. In 2006–2010, the construction of afforested work intensified, but industrial development and human activities affected the convergence pattern. The pattern of convergence in most regions between 2010 and 2018 can be attributed to the government's implementation of a series of key ecological protection projects, which led to a decrease in sensitivity to land degradation. The results of this study altogether suggest that the ESAI convergence analysis is an effective early warning method for land degradation sensitivity.

The Initial Errors in the Tropical Indian Ocean that Can Induce a Significant “Spring Predictability Barrier” for La Nina Events and Their Implication for Targeted Observations

Initial errors in the tropical Indian Ocean(IO-related initial errors) that are most likely to yield the Spring Prediction Barrier(SPB) for La Ni?a forecasts are explored by using the CESM model.These initial errors can be classified into two types.Type-1 initial error consists of positive sea temperature errors in the western Indian Ocean and negative sea temperature errors in the eastern Indian Ocean,while the spatial structure of Type-2 initial error is nearly opposite.Both kinds of IO-related initial errors induce positive prediction errors of sea temperature in the Pacific Ocean,leading to underprediction of La Nina events.Type-1 initial error in the tropical Indian Ocean mainly influences the SSTA in the tropical Pacific Ocean via atmospheric bridge,leading to the development of localized sea temperature errors in the eastern Pacific Ocean.However,for Type-2 initial error,its positive sea temperature errors in the eastern Indian Ocean can induce downwelling error and influence La Ni?a predictions through an oceanic channel called Indonesian Throughflow.Based on the location of largest SPB-related initial errors,the sensitive area in the tropical Indian Ocean for La Nina predictions is identified.Furthermore,sensitivity experiments show that applying targeted observations in this sensitive area is very useful in decreasing prediction errors of La Nina.Therefore,adopting a targeted observation strategy in the tropical Indian Ocean is a promising approach toward increasing ENSO prediction skill.

An Improved Parameter Dimensionality Reduction Approach Based on a Fast Marching Method for Automatic History Matching

History matching is a critical step in reservoir numerical simulation algorithms.It is typically hindered by difficulties associated with the high-dimensionality of the problem and the gradient calculation approach.Here,a multi-step solving method is proposed by which,first,a Fast marching method(FMM)is used to calculate the pressure propagation time and determine the single-well sensitive area.Second,a mathematical model for history matching is implemented using a Bayesian framework.Third,an effective decomposition strategy is adopted for parameter dimensionality reduction.Finally,a localization matrix is constructed based on the single-well sensitive area data to modify the gradient of the objective function.This method has been verified through a water drive conceptual example and a real field case.The results have shown that the proposed method can generate more accurate gradient information and predictions compared to the traditional analytical gradient methods and other gradient-free algorithms.