Sensitivity of WRF-Simulated 2 m Temperature and Precipitation to Physics Options over the Loess Plateau

Liu, Siliang

doi:https://doi.org/10.1155/2024/6633255

Advances in Meteorology

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Abstract Introduction Materials and Methods Results Conclusion and Discussion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2024 | Article ID 6633255 | https://doi.org/10.1155/2024/6633255

Sensitivity of WRF-Simulated 2 m Temperature and Precipitation to Physics Options over the Loess Plateau

Siliang Liu¹

Academic Editor: Roberto Coscarelli

Received05 Jul 2023

Revised22 Mar 2024

Accepted28 Mar 2024

Published02 May 2024

Abstract

The current paper evaluates the weather research and forecasting (WRF) model sensitivity to five different combinations of cumulus, microphysics, radiation, and planetary boundary layer (PBL) schemes over Loess Plateau for the period 2015, in terms of 2 m temperature and precipitation. The WRF configuration consists of a 10 km resolution domain nested in a coarser domain driven by European Center for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) data. The model simulated 2 m temperature and precipitation have been evaluated at daily and monthly scales with gridded observational dataset. The analysis shows that all experiments reproduce well the daily 2 m temperature, with overestimation particularly in the low-temperature range. Precipitation is less well simulated, with underestimation in all range, especially for intense rainfall. Comparing with ERA-Interim, WRF shows no clear benefit in simulating daily 2 m temperature while prominent improvement in simulating daily precipitation. WRF simulations capture the annual cycle of monthly 2 m temperature and precipitation with a warm bias and wet bias for most experiments in summer. Some reasonable configurations are identified. The “best” configuration depends on the criteria.

1. Introduction

Regional climate information is important for impact studies and climate change research. Comparing with global models, regional climate models (RCMs) are capable to produce climate simulation at higher spatial resolution with more realistic representation of surface heterogeneity and elevation. Therefore, RCMs are becoming the preferred tools for understanding climate at the regional scale [1–3].

Among RCMs developed by various institutes across the globe, the WRF model has been widely used by the science community for different regions [4–13]. It is also one of the RCMs being used for the coordinated regional climate downscaling experiment (CORDEX) [2] within the World Climate Research Program. A key feature of WRF is that it offers various physical parameterization schemes to be chosen [14]. However, there are limited guidance or experience on which configuration is suitable for a specific climate regime of simulated domain. In fact, there is no optimal configuration since model skill depends on the region, the parameter, the season, and the timescale, among others. Rather than identifying the best configuration for a given criterion, we may prefer to identify one or few configurations depicting satisfactory skills for multiple criteria.

The Loess Plateau, located in central northern China among the middle reach of Yellow River, occupies an area of over 640,000 km² land surface. It crosses arid, semiarid, and semihumid climate zones, with annual average temperature range of 4.3°C–14.3°C and average annual precipitation range of 200–750 mm from northwest to southeast [15]. Precipitation in the wet season (May through October) accounts for around 78% of the annual precipitation [16]. Regional climate information, particularly in terms of temperature and precipitation, is critical for the agricultural crop yield [17–20], hydrology, and water resources [21, 22]. Precipitation amount is critical important for rain-fed agriculture over this region, which accounts for 80% of the cultivated land [15]. In addition, extreme precipitation may lead to severe soil erosion over the Loess Plateau. Due to its fragile ecological environment and geographic features, the Loess Plateau is sensitive to climate change [23, 24]. Future climate change may exert even greater impacts on soil erosion, restored vegetation, limited water resources, and agricultural production [25–27].

A previous modeling study [28] demonstrates that RegCM4.3 is able to reproduce both the spatial and temporal features of the regional climate over the Loess Plateau. However, it tends to produce cold biases during winter and day biases during summer. Other studies focusing on China [29–31] or the Loess Plateau [32] using WRF largely overestimate the precipitation amount over the plateau. To provide reliable regional information over this region, accurate simulation of historical climate is a critical step. However, climate simulation biases and their sensivitiy to physical options are still not well understood [30, 32–34]. Although there are numerous studies conducting multiphysical sensitivity assessment using WRF over different regions [4–14, 35–38], the literature focusing on the Loess Plateau is still limited, which deserves further exploration.

The objective of this study is to evaluate the skill of different physics scheme combinations in reproducing regional climate over the Loess Plateau. Toward this end, starting from a reference model setup, general model performance is evaluated for one year period, testing the effects of different physical options on the simulation performance of daily, monthly, and seasonal values of 2 m temperature and precipitation. The results will provide useful information for dismissing less efficient parameterization schemes and selecting a suitable model configuration in this region. The overarching aim is to provide a basis for long-term simulations, impact studies, and future projections.

2. Materials and Methods

2.1. Model Configurations

Version 4.0 WRF model is employed with two-way nested domains: (1) outer coarse domain with 7140 km by 4770 km extent at 30 km grid spacing, covering the entire China mainland and surrounding area and (2) inner domain with 1420 km by 1150 km extent at 10 km grid spacing, focusing on the study area Loess Plateau. The coverage and geography of these two domains is illustrated in Figure 1. 33 vertical levels are set from surface up to 50 hPa in a terrain following the sigma coordinate. The detailed options of physical schemes and land surface model are listed in Table 1.

(a)

(b)

Lateral boundary condition (BC) and initial condition for outer domain are provided by ERA-Interim reanalysis [39], with horizontal spatial resolution of 0.75° × 0.75°, temporal interval of 6h, and 37 original pressure levels from 1000 to 1 hPa. Prescribed sea surface temperature (SST) from this dataset is updated each 6h as lower boundary conditions for the portions of the domain over the ocean.

The WRF model provides various options for the parameterization of (i) microphysics (MIC), (ii) cumulus parameterization (CP), (iii) surface layer condition (SLC), (iv) land surface model (LSM), and (v) planetary boundary layer (PBL). To create a WRF physics ensemble, a reference run using configuration recorded in a previous paper [40] is taken as the baseline, named as EXP_CAM. Then, the option for each scheme was changed once a time. Finally, a series of sensitivity experiments were designed, namely, EXP_DUD, EXP_BMJ, EXP_AC2, and EXP_3C (Table 2). These experiments were all carried out forced by identical initial condition and lateral boundary for one year spanning from December 1, 2014, to December 31, 2015, with the last month of 2014 treated as the spin-up period. As a normal monsoon year, the year 2015 was selected based on the examination of the East Asian summer monsoon index. Moreover, in terms of 2 m temperature and precipitation, the year 2015 is identified as normal year when investigating the anomaly from decadal observations.

2.2. CMFD Gridded Dataset

To evaluate the performance of WRF-simulated 2 m temperature and precipitation, the China meteorological forcing dataset (CMFD) downloaded from National Tibetan Plateau Data Center (https://data.tpdc.ac.cn) is utilized as reference. This newly released gridded meteorological dataset is developed with 0.1° spatial resolution at daily scale covering the period from 1979 to 2018 [41]. It is compiled based on gauge-observed data obtained from the China Meteorological Administration and other datasets such as satellite precipitation data and Global Land Data Assimilation System data [42]. High-resolution elevation data are introduced in the observed air temperature interpolation [42].

The gridded observation dataset described above is reprojected to Lambert projection and aggregated to 10 km in order to be consistent with WRF model outputs. Variables as daily 2 m temperature and daily precipitation are investigated, which are further aggregated into monthly and seasonal averaged values. The entire year is divided into the following four seasons: winter (December–Feburary, DJF), spring (March–May, MAM), summer (June–August, JJA), and fall (September–November, SON).

2.3. Evaluation Metrics

Evaluation is an important process in order to assess the performance of regional climate simulations. In this regard, three metrics are utilized, namely, bias, root mean squared error (RMSE), and Pearson’s correlation coefficient (R) against gridded observations. These metrics are applied on daily time series of area-averaged value over the Loess Plateau, which are calculated as follows:where is the modeled value; is the observation-derived value; and is the number of grids.

Due to limited computation resources, the evaluation was conducted for the period of one single year 2015. The model quality assessment procedure focused on the capacity of the WRF runs to correctly represent the spatial and temporal structures of 2 m temperature and precipitation distributions. Therefore, the metrics were calculated for each grid box and accumulation period at daily scale for the current study against the correspondent regular gridded datasets.

2.4. Taylor Diagram

To evaluate the performance of different WRF configurations in simulating climatic conditions, the Taylor diagram [43] was generated for intercomparison in each season. It provides a concise statistical summary of spatial correlation (PCC), centered root-mean-square error (RMSE), and spatial standard deviation (STDV). Geometric relationship between these metrics allows that the performance of each configuration in comparison to reference can be displayed on the same diagram. A perfect simulation would be one with a centered RMSE equal to 0 and both the PCC and STDV close to 1. The azimuthal position of a symbol in the Taylor diagram gives information on the spatial correlation coefficient between the RCM results and the reference. The radial distances from the origin to each symbol are proportional to the pattern standard deviation normalized by the reference variance, thus reference located at value 1. The distances of each symbol (along concentric circles) from this reference point indicate the centered RMSE based on the RCM and reference data. The Taylor diagram reported in the present study was based on daily 2 m temperature and daily precipitation for area-averaged mean values, with CMFD-gridded observation as the reference.

3. Results

3.1. 2 m Temperature

3.1.1. Geographical Distribution of 2 m Temperature Bias

Figure 2 provides an overview on the spatial distribution of observed mean seasonal values and biases of ERA-Interim as well as WRF experiments and ensemble for average 2 m temperature. Observed 2 m temperature shows clear seasonal cycle, with highest values in JJA (20°C–28°C) and lowest in DJF (−10°C–6°C). The temperature decreases from south to north in general, with the highest in Guanzhong plain located at southern border and lowest in elevated west corner within QH province. In general, the WRF simulations reproduce well the spatial variability of 2 m temperature for each season. In comparison with ERA-Interim, WRF simulations produce more spatial details. The WRF simulations present similar bias patterns except EXP_DUD. EXP_DUD shows prominent cold bias over almost the entire plateau in DJF, MAM, and SON (with the exception of small regions as the southern corner and western elevated area), while slight positive or negative bias over the entire plateau in JJA. The other four WRF simulations all produce strong warm bias in JJA with similar patterns but different magnitude (the areas with lower elevation seem correspond to higher warm bias) while both positive and negative bias in DJF, MAM, and SON depending on the regions.

The statistics of 2 m temperature at seasonal scale for each individual WRF experiments are summarized in Table 3. It can be identified that the correlation coefficients are commonly over 0.90 (except 0.88 for EXP_AC2 and EXP_DUD in DJF) for WRF simulations, which are higher in JJA and MAM as compared with SON and DJF. They are higher than the correlations for ERA-Interim in all four seasons (among 0.74–0.80), which indicates the added value of dynamical downscaling. Cold biases are prominent for EXP_DUD, particularly in DJF (−2.2°C), MAM (−1.9°C), and SON (−1.6°C). They are also large for EXP_AC2, in DJF (−0.8°C) and SON (−1.6°C). RMSEs are usually among the range between 0.20 and 0.50°C.

3.1.2. Annual Cycle of Monthly Mean 2 m Temperature

Figure 3 shows the annual cycle of area-average WRF-estimated 2 m temperature, together with the observation and ERA-Interim. The ensemble mean of the individual experiments are also calculated and presented. Overall, WRF simulations generate a good reproduction of observed monthly variation of 2 m temperature. The observation is generally within the range of model spread.

As illustrated in Figure 4, most WRF configurations produce a warm bias, particularly during May–September, which can reach over 2°C. The only exception is EXP_DUD, which estimates clearly lower 2 m temperature than all other experiments. This might be attributed to that the Dudhia scheme is a very simple parameterization compared to other options for shortwave radiation scheme [44, 45]. The approach considered in this scheme is simpler than in other parameterizations because the radiative transfer equation as well as the spectral integration are not solved explicitly [46]. It corresponds well to the observation in summer while underestimates for other period along the year. The annual cycle is reproduced similarly by ERA-Interim and WRF.

3.1.3. Percentile Plot of Daily 2 m Temperature

All daily values are considered to calculate ten percentiles (1^st, 5^th, 10^th, 25^th, 50^th, 75^th, 90^th, 95^th, 99^th, and 99.9^th) for 2 m temperature. As shown in Figure 5, minor differences are observed among the explored configurations. The spread of WRF-simulated 2 m temperature with different physical options is generally less than 5°C.

In comparison with ERA-Interim, 2 m temperature within the range of 25%–75% is improved for most WRF simulations except EXP_DUD. Nevertheless, the extreme low and extreme high temperature are not improved or even worse for all WRF simulations. ERA-Interim seems to well describe the percentiles in the high temperature range (75^th, 90^th, 95^th, 99^th, and 99.9^th), while WRF simulates tend to produce a warmer extreme in summer. Both ERA-Interim and WRF underestimate the percentiles in low temperature range (1^st, 5^th, and 10^th), and cold bias in winter is larger in WRF simulations than ERA-Interim.

3.1.4. Time Series of Daily 2 m Temperature

To demonstrate skill in the simulation of temporal variability, area-averaged time series of daily average 2 m temperature over the Loess Plateau are presented as colored lines in Figure 6. The time series show that WRF is skillful at capturing the variability of the observed 2 m temperature. Nevertheless, biases can be identified, especially in summer and winter. In general, almost all the five configurations lead to warm bias during the period from Julian day 160–260. Significant negative bias can be identified for EXP_DUD, especially in winter period (Julian day 15–80 and 310–365).

3.2. Precipitation

3.2.1. Geographical Distribution of Precipitation Bias

Figures 2 and 7 provide an overview on the spatial distribution of the observed mean seasonal value and biases of ERA-Interim and WRF experiments for area-averaged precipitation. Observed precipitation shows clear seasonal difference, with highest amount in JJA and lowest in DJF. The precipitation gradient decreasing from southeast to northwest can be discerned from CMFD observation, particularly in JJA, SON, and MAM. The high rainfall center, located along the southern border, is prominent in these three seasons. Both ERA-Interim and WRF simulations generally reproduce the spatial variability precipitation for each season. Nevertheless, ERA-Interim generally presents dry bias over most parts of the plateau in MAM, JJA, and SON, while WRF simulations produce wet or dry bias depending on the season and location. In JJA, the WRF simulations present similar bias patterns (large wet bias) over SX province located in the eastern part of the plateau while different patterns in other parts of the plateau. For instance, EXP_DUD and EXP_AC2 similarly produce wet bias over almost the entire plateau (with the exception of the rainfall center area identified from the observation), with the extreme value over 3 mm/d. EXP_CAM, EXP_BMJ, and EXP_3C simulate dry bias over large area outside SX province (the eastern part). In DJF, MAM, and SON, the precipitation bias patterns by five WRF simulations are similar.

Similar to Table 3, the statistics of precipitation at seasonal scale for ERA-Interim reanalysis and each individual WRF experiments are summarized in Table 4. For WRF experiments, it can be identified that the correlation coefficients are much lower than that of 2 m temperature. They are among the range of 0.29–0.58, which are higher in MAM than other seasons. In SON, the correlations of WRF experiments are higher or equal than ERA-Interim while not in the case of DJF, MAM, and JJA. Unlike the dry bias of ERAI (−0.79 mm/d), wet biases dominate in summer (JJA) for all WRF experiments. EXP_AC2 holds the highest wet bias (1.42 mm/d), followed by EXP_DUD (1.11 mm/d).

3.2.2. Annual Cycle of Monthly Mean Precipitation

The annual cycles of WRF-simulated precipitation are presented in Figure 8, together with the observation and ERA-Interim data. In general, the seasonal pattern of rainfall rise is captured by the simulations, exhibiting high precipitation during summer and low during winter, with clear discrepancies in the rates. All the experiments capture the monthly variation during September–May with little inter-model variation.

As illustrated in Figure 9, EXP_BMJ holds the highest amount simulated, which clearly overestimates rainfall in this period. In summer, simulations varied, which broadly overestimate precipitation during JJA. EXP_AC2 and EXP_DUD simulate the highest precipitation amount in this period, prominently overestimate the amount. Interestingly, the fall from July to August is only captured by EXP_3C (Figure 8). Indeed, the modeling of precipitation is one of the main challenges in high-resolution regional models. Simulations of precipitation are less reliable compared with 2 m temperature. The WRF-simulated precipitation is generally higher than ERA-Interim, particularly in summer. It convinces the add value of dynamical downscaling.

3.2.3. Percentile Plot of Daily Precipitation

All daily values are taken into account to calculate eight percentiles (50^th, 60^th, 70^th, 75^th, 80^th, 90^th, 95^th, and 99^th) for precipitation. Figure 10 displays the percentiles for WRF, ERA-Interim, and observations. Estimations of precipitation show a clear underestimation for both WRF and ERA-Interim. The spread of WRF estimates in precipitation is larger for high percentile than low percentile, which indicates that the different choice of options is essentially important for extreme values.

In comparison with ERA-Interim, precipitation is improved for all combinations using WRF, which reduce the underestimation. The percentiles in the extreme high range are the greatest improved. This is in line with the description of the dynamical downscaling add-value, enhancement in characterizing extremes [1]. The best experiment in simulating precipitation within the range above 90^th is EXP_DUD and the best one among 70^th and 80^th is EXP_BMJ.

3.2.4. Taylor Diagram of Daily Precipitation

Statistical metrics for ERA-Interim and the five WRF experiments are computed for each season and displayed in Figure 11 as Taylor diagram in order to provide a synthetic summary how closely the WRF simulations match the observation. It indicates that simulated precipitation with a correlation coefficient above 0.5 in MAM and SON and within the range between 0.3 and 0.5 in DJF and JJA. The correlations of WRF simulations are higher than ERA-Interim in JJA while comparable to ERA-Interim in other seasons. CRMSE is around 1.0 in MAM and SON, larger in JJA (2.0–4.0), and even greater in DJF (mostly above 4.0). It indicates that the variation is reasonably captured in MAM and SON while much higher in JJA and DJF, comparing with the observation. However, WRF simulations often generate higher CRMSE than ERA-Interim. It is difficult to identify the best physical combination since experiment performance varies among seasons. Generally, they cluster much more closely in MAM and SON while spread widely in DJF and JJA. EXP_DUD seems to be a fine choice when taking all three metrics into consideration for four seasons.

(a)

(b)

(c)

(d)

Figure 11

Taylor diagrams of area-averaged WRF simulation and ERA-Interim data with respect to observed precipitation in the year 2015 (full names of the individual experiment are given in Table 2) for (a) DJF (December–February), (b) MAM (March–May), (c) JJA (June–August), and (d) SON (September–November). Calculations are applied to present the temporal variability of daily precipitation. (Three statistics determine the relative places of the models: the Pearson’s correlation coefficient (curved axes), the centered RMS error (gray contours), and the standard deviation (Oy-axis). The model fitting best with observations will lie the nearest to the Ox-axis.

The relative low correlation coefficient indicates that satisfactory precipitation simulations over the Loess Plateau have remained a challenge. Precipitation is difficult to simulate due to complex physical processes and high spatial-temporal variability. Moreover, the topography effects in this hilly region also complex the simulation results.

4. Conclusion and Discussion

In this paper, regional climate simulations using WRF with different physical scheme options were carried out to produce one year long simulations over the Loess Plateau. The performances were evaluated against CMFD-gridded observational dataset, focusing on 2 m temperature and precipitation. The objective is to assess the general performance of the model and examine the sensitivity to different configurations. The results will provide useful information to select appropriate configuration over the Loess Plateau. The main conclusions are listed as follows:(1)WRF simulations reproduce well the spatial variability of 2 m temperature for all seasons. Similar bias patterns can be identified for all experiments except EXP_DUD, which shows prominent cold bias over almost the entire plateau in DJF, MAM, and SON.(2)WRF simulations reasonably capture the spatial variability of precipitation for all seasons, with large wet biases in summer. In JJA, the WRF simulations present similar bias patterns (large wet bias) over SX province located in the eastern part of the plateau while different patterns in other parts of the plateau.(3)WRF simulations reproduce well observed monthly variation of 2 m temperature. However, most WRF configurations produce a warm bias, particularly during May–September, which can reach over 2°C. The only exception is EXP_DUD, which estimates clearly lower 2 m temperature than all other experiments.(4)The seasonal pattern of rainfall rise is generally captured by the simulations, exhibiting high precipitation rate in summer and low value in winter. The WRF-simulated precipitation is generally higher than ERA-Interim, particularly in summer.(5)At daily scale, minor differences are observed for 2 m temperature among the explored configurations. In comparison with ERA-Interim, 2 m temperature within the range of 25%–75% is improved for most combinations with the exception of EXP_DUD. Nevertheless, the extreme low and extreme high temperature are not improved or even worse for WRF simulations.(6)As for daily precipitation, it is clearly underestimated for both WRF and ERA-Interim. In comparison with ERA-Interim, precipitation is improved for all combinations using WRF, which reduce the underestimation.(7)Although it is difficult to choose one scheme as the best one for reproducing 2 m temperatures, the EXP-CAM simulation could be a good configuration, attending to the bias, although the rest of the experiments present similar results.(8)As for precipitation, EXP_DUD seems to be a fine choice when taking all three metrics into consideration for four seasons.

A previous modeling study focuses on the Loess Plateau using RegCM [28] evaluated against CN05 dataset [47]. Their simulation tends to produce cold biases during winter and underestimate precipitation during summer. Our results are different with their conclusion, yet using different RCM model, configuration and gridded dataset compared. Some other studies have used WRF to simulate precipitation over China [29–31] or the Loess Plateau [32], and large wet biases are found over the plateau. Our simulation of dominant wet bias, particularly in summer, is broadly consistent with these results.

Numerous studies have been conducted worldwide with the WRF model to select the most appropriate configuration for regional climate simulations [37, 48–53]. From the common findings of these studies, it is evident that the WRF model is capable to represent regional climate although systematic biases can be identified in the simulations. Simulation performances have been found to be sensitive to physics parameterizations [11, 54–56] and land surface models [6, 57]. Optimal performance of WRF requires a computational expensive investigation over different combinations of parameterization schemes which vary from region to region.

It is important to point out several limitations in the present work. Due to limited computation resources, the period selected for simulation is only one year, which is not long enough to obtain climatic robust evidence. Interannual variation is not investigated since one year simulation outputs are analyzed. Our findings need to be further confirmed by future research with continuous simulation on a climatic (30 years) timescale.

Horizontal grid spacing is an issue which is worth to discuss. Numerous studies have addressed the numerical sensitivity to model grid spacing [58, 59]. Convection-permitting models (CPMs) are found to provide a better representation of convective processes on both climate and numerical weather prediction (NWP) timescales [60–71]. Although CPMs do not fully resolve convection, they can represent larger storms and mesoscale organization of convection explicitly on the model grid much better than the parameterizations of convection [72]. Future study will carry out simulations using CPMs at grid spacing less than ∼5 km instead of 10 km, with computational cost taken into account.

In addition to physical configuration and grid spacing, the choice of domain location and geometry also impacts the model results [73–81]. Jones et al. [74] argued that the regional model domain should be larger than the region of interest to allow full development of small-scale features over the area of interest since a smaller domain size may suppress the development of key mesoscale features. In contrast to this, Bhaskaran et al. [75] showed that the regional model domain should not be so large that the simulation deviates significantly from the large-scale features of the driving model. The study by Seth and Giorgi [77] noticed in their study that the performance of a regional climate model is subjected to the careful selection of the domain for its specific application.

Another factor influencing the modeling results is model internal variability, which results from nonlinearities in the model physics and dynamics [82, 83]. It is defined as the difference between realization members where the only differences are the initial conditions. A multimember ensemble approach is commonly used by the regional climate modeling community to disentangle the reproducible and irreproducible components of climate variability [82, 84]. Some initial condition ensembles, commonly found in weather forecasting and climate modeling [85], have addressed internal variability within RCMs [82, 86–91].

Finally, due to limited computation resources, only five combinations of physical options were compared in the current study, generated by varying one option for each physical scheme from a reference configuration. To fully understand the impact of physical options, more combinations should be investigated.

Data Availability

The data that support the findings of this study are openly available. CMFD gridded dataset is accessible at https://data.tpdc.ac.cn. ERA-Interim surface air temperature and precipitation can be downloaded from https://cds.climate.copernicus.eu/.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The author would like to thank Dr. Werner at NCAR for her helpful advice on various technical issues relative to WRF simulation. All the model runs described in this study were carried out with the facilities of the Center for High Performance Computing at Wuhan University. This research was funded by the National Natural Science Foundation of China, grant number 42001359.

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