Spin-up Time Research Articles

Spin-up characteristics with three types of initial fields and the restart effects on forecast accuracy in the GRAPES global forecast system

Abstract. The spin-up refers to the dynamic and thermal adjustments made at the initial stage of numerical integration in order to reach a statistical equilibrium state. The analyses on the characteristics and effects of spin-ups are of great significance for optimizing the initial field of the model and improving its forecast skills. In this paper, three different initial fields are used in the experiments: the analysis field of four-dimensional variational (4D-VAR) assimilation, the 3 h prediction field in the operational forecasting system, and the Final (FNL) Operational Global Analysis data provided by National Centers for Environmental Prediction (NCEP). Following this, the characteristics of spin-ups in the version 2.3.1 of GRAPES (Global/Regional Assimilation and Prediction System) global forecast system (GRAPES_GFS2.3.1) under different initial fields are compared and analyzed. In addition, the influence of the lost cloud-field information on the spin-up and forecast results of the GRAPES model in the current operation is discussed as well. The results are as follows. With any initial field, the spin-up of GRAPES_GFS2.3.1 has to go through two stages – the dramatic adjustment in the first half-hour of integration and the slow dynamic and thermal adjustments afterwards. The spin-up in GRAPES_GFS2.3.1 lasts for at least 6 h, and the adjustment is gradually completed from lower to upper layers in the model. Therefore, in the evaluation of the GRAPES_GFS2.3.1, the forecast results in the first 6 h should be avoided, and the GRAPES_GFS2.3.1 with its own analysis field performs better than the one using FNL reanalysis data for the cold start in the spin-up because the variations in amplitude of the temperature and humidity tendency are smaller and the spin-up time is slightly shorter. Based on the 4D-VAR assimilation analysis field, the forecast in the operational model is artificially interrupted and restarted after 3 h of integration. In this process, as the cloud-field information is not retained, the spin-up should repeat in the model. The characteristics of spin-up are mostly consistent with those using the 4D-VAR assimilation analysis field as the initial field. However, as the cloud-field information is not retained in the current operation, the hydrometeor content in the atmosphere at the early stage of the forecast is underestimated, affecting the calculation accuracy of the radiation and causing a systematic positive bias of temperature and geopotential height fields at 500 hPa. In addition, the precipitation is also underestimated at the early stage of the simulation, affecting the forecast of typhoon tracks.

Application of PFI-4DVar Data Assimilation Technique to Nowcasting of Numerical Model

Nowcasting is mainly based on radar echo or satellite image extrapolation method. However, the prediction ability of extrapolation method decreases with time, because this method cannot describe the physical mechanism during the occurrence, development and extinction of severe convective weather systems. Considering that the prediction ability of numerical model improves with time, the nowcasting system should be based on numerical forecast model. And appropriate data assimilation technology can be used to produce a more accurate initial field, making the integral forecast results closer to the reality. The PFI-4DVar assimilation method (four-dimensional variational technology under physical filter initialization) can filter in the process of assimilation rather than model integration, thus shortening the model spin-up time and getting a more dynamic and physically coordinated analysis field. Therefore, PFI-4DVar assimilation method not only improves model prediction results, but also makes initial field closer to observations, which is very suitable for nowcasting.Using WRF model and WRFDA assimilation system, effects of PFI-4DVar on prediction ability of numerical nowcasting are explored. Through the precipitation case in North China on 11 August 2018, prediction results in control and assimilation tests are discussed. According to ETS scores, the precipitation prediction of assimilation test is closer to the observation compared with control test. The water vapor in assimilated ground and sounding data, the dynamic field in assimilated radar radial wind data and the appropriate cumulus parameterization scheme make the amplitude of divergence in high-level and convergence in low-level in analysis field of assimilation test much stronger than those in background field, thus creating vertical motion. Moreover, the precipitation of assimilation test is mainly caused by process of cumulus.A batch test is carried out on 17 precipitation cases of North China in August 2018. It shows that PFI-4DVar can significantly improve the prediction ability for short precipitation (especially large order precipitation) and timely predict the fall area of heavy rain or rainstorm. After assimilation, ETS scores of 6-hour accumulated precipitation (greater than 25.0 mm) in batch test increase from 0.125 to 0.190, and ETS scores of 6-hour accumulated precipitation (greater than 60.0 mm) increase from 0.016 to 0.081. PFI-4DVar significantly improves the precipitation nowcasting. Calculations are reduced by selecting 12-minute assimilation time window, which greatly saves computational resources. And the time of assimilation test is shortened, ensuring the time efficiency of 6-hour forecast. Therefore, PFI-4DVar can improve and enhance the prediction ability of precipitation nowcasting.

Assimilating radar radial wind and reflectivity data in an idealized setup of the COSMO-KENDA system

An idealized framework for radar data assimilation has been developed based on the operational data assimilation system of the Deutscher Wetterdienst (DWD) for the regional convection-permitting model of COSMO (COnsortium for Small-scale MOdelling), coupled with an Efficient Modular VOlume scanning RADar Operator (EMVORADO). The data assimilation scheme is the Local Ensemble Transform Kalman Filter (LETKF). The idealized framework is used to explore differences between radial wind and reflectivity observations in storm-scale data assimilation by conducting a series of twin experiments. First, it is shown for both types of data that using the estimated observation errors obtained by the Desroziers method help to reduce the model state errors during cycles. Assimilating only radial winds results in smaller errors in wind components and assimilating reflectivities only results in smaller errors in temperature and microphysical variables, and the latter one requires a shorter spinup time to reconstruct precipitating systems. Both radial wind and reflectivity data are useful to reconstruct the dynamical structure of supercells described by the supercell detection index but radial wind data are more important. However, a considerable amount of spurious convective cells arise if only radial winds are assimilated. Assimilating reflectivities is able to efficiently reduce the spurious convection due to the assimilation of no reflectivity data. Additionally, it is shown that the data assimilation could cause significant biased increases in divergence, vorticity and total specific mass of microphysical variables. Assimilation of radial winds leads to lower increases in divergence and vorticity, while assimilation of reflectivities leads to lower increases in total specific mass. The amount of increase depends on the specification of the observation error statistics and on the number of assimilated data. The 6-h forecasts are skillful for both assimilating radial winds only and assimilating reflectivity only, while the latter one is even better since the skills of the former one are heavily penalized for spurious convection. Overall, radial wind and reflectivity data complement each other, assimilation of both data simultaneously results in the smallest state errors and the lowest biased increase in divergence, vorticity and total specific mass during cycles and subsequently the best 6-h forecasts.

Discovery of Delayed Spin-up Behavior Following Two Large Glitches in the Crab Pulsar, and the Statistics of Such Processes

Abstract Glitches correspond to sudden jumps of rotation frequency (ν) and its derivative ( ) of pulsars, the origin of which remains not well understood yet, partly because the jump processes of most glitches are not well time-resolved. There are three large glitches of the Crab pulsar, detected in 1989, 1996, and 2017, which were found to have delayed spin-up processes before the normal recovery processes. Here we report two additional glitches of this pulsar that occurred in 2004 and 2011 for which we discovered delayed spin-up processes, and present refined parameters of the largest glitch, which occurred in 2017. The initial rising time of the glitch is determined as <0.48 hr. The two glitches that occurred in 2004 and 2011 had delayed spin-up time scales (τ 1) of 1.7 ± 0.8 days and 1.6 ± 0.4 days, respectively. We also carried out a statistical study of these five glitches with observed spin-up processes. We find that the Δν versus relation of these five glitches is similar to those with no detected delayed spin-up process, indicating that they are similar to the others in nature except that they have larger amplitudes. For these five glitches, the amplitudes of the delayed spin-up process ( ) and recovery process (Δν d2), their time scales (τ 1, τ 2), and permanent changes in spin frequency (Δν p) and total frequency step (Δν g) have positive correlations. From these correlations, we suggest that the delayed spin-up processes are common for all glitches, but are too short and thus difficult to be detected for most glitches.

The impact of initial conditions on convection-permitting simulations of a flood event over complex mountainous terrain

Abstract. Western Norway suffered major flooding after 4 d of intense rainfall during the last week of October 2014. While events like this are expected to become more frequent and severe under a warming climate, convection-permitting scale models are showing their skill with respect to capturing their dynamics. Nevertheless, several sources of uncertainty need to be taken into account, including the impact of initial conditions on the precipitation pattern and discharge, especially over complex, mountainous terrain. In this paper, the Weather Research and Forecasting Model Hydrological modelling system (WRF-Hydro) is applied at a convection-permitting scale, and its performance is assessed in western Norway for the aforementioned flood event. The model is calibrated and evaluated using observations and benchmarks obtained from the Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The calibrated WRF-Hydro model performs better than the simpler conceptual HBV model, especially in areas with complex terrain and poor observational coverage. The sensitivity of the precipitation pattern and discharge to poorly constrained elements such as spin-up time and snow conditions is then examined. The results show the following: (1) the convection-permitting WRF-Hydro simulation generally captures the precipitation pattern/amount, the peak flow volume and the timing of the flood event; (2) precipitation is not overly sensitive to spin-up time, whereas discharge is slightly more sensitive due to the influence of soil moisture, especially during the pre-peak phase; and (3) the idealized snow depth experiments show that a maximum of 0.5 m of snow is converted to runoff irrespective of the initial snow depth and that this snowmelt contributes to discharge mostly during the rainy and the peak flow periods. Although further targeted experiments are needed, this study suggests that snow cover intensifies the extreme discharge instead of acting as a sponge, which implies that future rain-on-snow events may contribute to a higher flood risk.

Spin Retry Count Relation with Other hdd Parameters

The change of the SMART parameter 10 Spin retry count values depending on the operating time is considered; this parameter characterizes the number of repeated attempts to spin the disks up to operating speed if the first attempt was unsuccessful. This parameter is critical in the sense that if the value of the attribute increases, then the likelihood of malfunctions in the mechanical part of the hard disk drives is high. The scientific task of the study is to establish the relationship between this parameter in failed hard drives and the values of other reliability parameters for information stores from various manufacturers. In the course of the study, the drives of the HGST, Hitachi, Samsung, ST, Toshiba, WDC trademarks operated in the Backblaze largest commercial data centre were analysed. As a result of the analysis, the relationship between the specified parameter and such parameters as 3 Spin-up time (time of spinning the disk package from standstill to operating speed), 4 Start/stop count (counting the spindle start/stop cycles), 12 Power cycle count (number of full drive switching on/off cycles), 192 Power-off retract count (the number of shutdown cycles, including emergency), 193 Load cycle count (the number of magnetic head block moves in the parking zone/in working position cycles). It is shown that the nature of the change in the values of the considered parameters depends on the manufacturer of the hard drives. It is proposed to carry out an individual assessment of the information storage device rotation mechanism reliability using the parameters identified as a result of the study.

A new approach combining a simplified FLEXPART model and a Bayesian-RAT method for forecasting PM10 and PM2.5

In this study, we evaluated atmospheric particulate matter (PM) concentration predictions at a regional scale using a simplified Lagrangian particle dispersion modeling system and the Bayesian and multiplicative ratio correction optimization (Bayesian-RAT) method to improve the mixing ratio forecast of PM10 and PM2.5. We first examined the forecast performance of the LPD (i.e., the simplified FLEXPART model combined with the Bayesian-RAT method) by comparing the model predictions with the PM concentration observations from 95 observation stations in Xingtai city and its surrounding areas. The first 2 months (i.e., Oct. and Nov. 2017) of the study period represented the typical spin-up time period, and the analysis period was December 2017. The LPD forecast system was much better (correlation coefficient: R=0.64 vs. 0.48 and 0.67 vs. 0.50 for PM10 and PM2.5, respectively; root mean square error: RMSE = 74.98 vs. 105.96 μg/m3for PM10 and 54.89 vs. 72.81 μg/m3 for PM2.5) than the pre-calibration results. We also compared the LPD forecasting model with other models (WRF-Chem and Camx) using data from monitoring stations in Xingtai, China, and the LPD forecasting model had higher accuracy than the other models. In particular, the RMSE scores for hourly PM10 concentrations were reduced by 36.51% and 42.21% compared to WRF-Chem and to Camx, respectively. The PM2.5 forecast results, as in the case of PM10, showed a better performance when applying the LPD model to the data from the monitoring stations. The RMSE was reduced by 26.44% and 18.47% relative to the WRF-Chem and Camx, respectively. The results confirm that there is much advantage of the LPD forecast system for predicting PM and may be for other pollutants.

Diffuse X-ray emission around an ultraluminous X-ray pulsar

Ultraluminous X-ray sources (ULXs) are extragalactic X-ray emitters located off-centre of their host galaxy and with a luminosity in excess of a few 1039 erg s−1, if emitted isotropically1,2. The discovery of periodic modulation revealed that in some ULXs the accreting compact object is a neutron star3–7, indicating luminosities substantially above their Eddington limit. The most extreme object in this respect is NGC 5907 ULX-1 (ULX1), with a peak luminosity that is 500 times its Eddington limit. During a Chandra observation to probe a low state of ULX1, we detected diffuse X-ray emission at the position of ULX1. Its diameter is 2.7 ± 1.0 arcsec and contains 25 photons, none below 0.8 keV. We interpret this extended structure as an expanding nebula powered by the wind of ULX1. Its diameter of about 200 pc, characteristic energy of ~1.9 keV and luminosity of ~2 × 1038 erg s−1 imply a mechanical power of 1.3 × 1041 erg s−1 and an age of ~7 × 104 yr. This interpretation suggests that a genuinely super-Eddington regime can be sustained for timescales much longer than the spin-up time of the neutron star powering the system. As the mechanical power from a single ULX nebula can rival the injection rate of cosmic rays of an entire galaxy8, ULX nebulae could be important cosmic ray accelerators9. Diffuse X-ray emission is detected around an ultraluminous X-ray source, interpreted as a wind-powered expanding nebula. Its energetics suggests that a super-Eddington regime can be longer than the spin-up time of the central neutron star.

General spin-up time distribution for energy-aware IaaS cloud service models

Cloud computing services provided over the Internet are realized by servers in physically distributed data centers that consume tremendous power for operational and maintenance purposes. To minimize energy consumption, modern cloud systems adopt intelligent sever power switching with thresholds based on the current system load and are bounded by the number of idle servers. The time taken to power on physical or virtual servers, known as the spin-up time, can significantly impact the delay incurred in service delivery and elasticity of real cloud platforms. In this paper, we model and assess the asymptotic performance of an energy-aware cloud data center assuming general distribution for server spin-up time. The waiting time of a newly arriving request is defined in the service-level agreement (SLA) and for each busy server, the fixation time distribution derived from an absorbing birth-and-death process characterizes the impact of thresholds. Simulation results show that the proposed model calculates the probability of SLA violation for different threshold values with less than 0.5% error.

Wind Estimate by a Mesoscale Model

Wind is an energy source that has been used for a long time to reach many goals, but has recently become very important in electricity generation. In Rio Grande, a city located in Rio Grande do Sul state, in the extreme south of Brazil, wind forecast is important due to several weather phenomena of different time and space scales that act in the region. They make wind energy be one of the most difficult energy sources to be forecasted, since small variations of wind estimates may result in large differences in energy that is effectively generated. This study aimed at evaluating the performance of a mesoscale model to simulate wind intensity and estimating electricity production by aerogenerators, with and without nudging. This method enabled simulations to be carried out for a long time, with no spin-up time. The period of study is from June 9th to 14th, 2017, with two horizontal grids nested, of 3 and1 km. Results showed that the model is more accurate when nudging rather than spin-up is used. Nevertheless, the model still leads to errors in power forecast, by comparison with the wind power measured at the aerogenerator.

Adaptation of NEMO-LIM3 model for multigrid high resolution Arctic simulation

High-resolution regional hindcasting of ocean and sea ice plays an important role in the assessment of shipping and operational risks in the Arctic Ocean. The ice–ocean model NEMO-LIM3 was modified to improve its simulation quality for appropriate spatio-temporal resolutions. A multigrid model setup with connected coarse- (14 km) and fine-resolution (5 km) model configurations was devised. These two configurations were implemented and run separately. The resulting computational cost was lower when compared to that of the built-in AGRIF nesting system. Ice and tracer boundary-condition schemes were modified to achieve the correct interaction between coarse- and fine grids through a long ice-covered open boundary. An ice-restoring scheme was implemented to reduce spin-up time. The NEMO-LIM3 configuration described in this article provides more flexible and customisable tools for high-resolution regional Arctic simulations.

Effects of High Resolution and Spinup Time on Modeled North Atlantic Circulation

AbstractThe influence of a high horizontal resolution (5–15 km) on the general circulation and hydrography in the North Atlantic is investigated using the Finite Element Sea Ice–Ocean Model (FESOM). We find a stronger shift of the upper-ocean circulation and water mass properties during the model spinup in the high-resolution model version compared to the low-resolution (~1°) control run. In quasi equilibrium, the high-resolution model is able to reduce typical low-resolution model biases. Especially, it exhibits a weaker salinification of the North Atlantic subpolar gyre and a reduced mixed layer depth in the Labrador Sea. However, during the spinup adjustment, we see that initially improved high-resolution features partially reduce over time: the strength of the Atlantic overturning and the path of the North Atlantic Current are not maintained, and hence hydrographic biases known from low-resolution ocean models return in the high-resolution quasi-equilibrium state. We identify long baroclinic Rossby waves as a potential cause for the strong upper-ocean adjustment of the high-resolution model and conclude that a high horizontal resolution improves the state of the modeled ocean but the model integration length should be chosen carefully.

Multi-Physics Ensemble versus Atmosphere–Ocean Coupled Model Simulations for a Tropical-Like Cyclone in the Mediterranean Sea

Between 19 and 22 January 2014, a baroclinic wave moving eastward from the Atlantic Ocean generated a cut-off low over the Strait of Gibraltar and was responsible for the subsequent intensification of an extra-tropical cyclone. This system exhibited tropical-like features in the following stages of its life cycle and remained active for approximately 80 h, moving along the Mediterranean Sea from west to east, eventually reaching the Adriatic Sea. Two different modeling approaches, which are comparable in terms of computational cost, are analyzed here to represent the cyclone evolution. First, a multi-physics ensemble using different microphysics and turbulence parameterization schemes available in the WRF (weather research and forecasting) model is employed. Second, the COAWST (coupled ocean–atmosphere wave sediment transport modeling system) suite, including WRF as an atmospheric model, ROMS (regional ocean modeling system) as an ocean model, and SWAN (simulating waves in nearshore) as a wave model, is used. The advantage of using a coupled modeling system is evaluated taking into account air–sea interaction processes at growing levels of complexity. First, a high-resolution sea surface temperature (SST) field, updated every 6 h, is used to force a WRF model stand-alone atmospheric simulation. Later, a two-way atmosphere–ocean coupled configuration is employed using COAWST, where SST is updated using consistent sea surface fluxes in the atmospheric and ocean models. Results show that a 1D ocean model is able to reproduce the evolution of the cyclone rather well, given a high-resolution initial SST field produced by ROMS after a long spin-up time. Additionally, coupled simulations reproduce more accurate (less intense) sea surface heat fluxes and a cyclone track and intensity, compared with a multi-physics ensemble of standalone atmospheric simulations.

The value of simplified models for spin up of complex models with an application to subsurface hydrology

Spinning up large-scale coupled surface-subsurface numerical models can be a time and resource consuming task. If an uninformed initial condition is chosen, the spin-up can easily require 20 years of repeated simulations on high-performance computing machines. In this paper we compare the classical approach of starting from a fixed shallow depth to groundwater (here 3 m) with three more informed approaches for the definition of initial conditions in the spin up. In the first of these three approaches, we start from a known-steady state groundwater table, calculated with a 2-D groundwater model and the yearly net recharge, and combine it with an unsaturated zone that assumes hydrostatic conditions. In the second approach, we start from the same groundwater table combined with vertical profiles in the unsaturated zone with uniform vertical flow identical to the groundwater recharge. In the third approach we calculate a dynamic steady state from a simplified subsurface model combining a transient 2-D groundwater model with a limited number of 1-D transient unsaturated zone columns on top. Results for spinning-up a 3-D Parflow-CLM model using the different initial conditions show that large gains can be made by considering states in groundwater and the vadose zone that are consistent, i.e. where groundwater recharge and the vertical flux in the vadose zone agree. By this, the spin-up time was reduced from about 10 years to about 3 years of simulated time. In the light of seasonal fluctuations of net recharge, using the transient approach showed more stable results.

The Onset of Resolved Boundary-Layer Turbulence at Grey-Zone Resolutions

Numerical weather prediction (NWP) models are now capable of operating at horizontal resolutions in the 100-m to 1-km range, a grid spacing similar in scale to that of the turbulent eddies present in the atmospheric convective boundary layer (CBL). Known as the ‘grey zone’ of turbulence, this regime is characterized by significant contributions from both the resolved and subgrid components to represent the dominant motions of the system. This study examines how the initiation of resolved turbulence – a concept commonly referred to as ‘spin-up’– can be delayed during the evolution of a simulated CBL in the grey zone. We identify the importance of imposed pseudo-random perturbations of potential temperature (theta ) for the development of the resolved fields showing that without such perturbations, resolved turbulence does not become established at all. When the perturbations are organized, spin-up can develop more rapidly, and we find that the earliest spin-up times can be achieved by applying an idealized profile of variance to derive the theta perturbation values. The perturbation structures are shown to be most effective when applied at intervals following the mixed-layer time scale, t_{*}, rather than perturbing only at the initial time. We also propose a modification to the three-dimensional Smagorinsky turbulence closure, in which the Smagorinsky constant is replaced by a scale-dependent coefficient. Both the approaches of: (1) applying structured theta perturbations, and (2) using a dynamically-evolving Smagorinsky coefficient are shown to encourage faster spin-up independently of each other, but the best results clearly emerge when the two methods are applied concurrently.

Effect of hydrometeor species on very-short-range simulations of precipitation using ERA5

The impact of five hydrometeor species provided by the fifth generation of ECMWF ReAnalysis (ERA5) on the skill of precipitation forecasts is investigated in very-short-range numerical weather prediction modeling using WRF. Three sets of experiments using different numbers of hydrometeors as input in the initial and lateral boundary conditions were designed for 6-h forecasts run over the region of Korea in East Asia during two one-month periods of the summer and winter seasons: specific humidity only, additional specific cloud liquid water and cloud ice water, and additional specific rain water and snow water. The 6-class Thompson microphysics parameterization scheme that uses prognostic condensate variables was adopted for forecasts to represent the effect of using these hydrometeors in the initial conditions. Simulated precipitation was verified against surface observations in terms of both categorical scores based on contingency tables and object-oriented measures. The evaluation of the precipitation skill reveals that the forecasts using multiple hydrometeors as input generally show increased skill of precipitation forecasts compared to forecasts using only specific humidity as input. It is found that the increase of precipitation forecasting skill appears both in winter and in summer. Using more hydrometeors in the initial conditions reduces the spin-up time and accelerates the occurrence of resolved precipitation in the model. This effect is clearly shown in the first one to two hours of the forecast. Afterwards, the precipitation forecast skill is similar to that of forecasts that use only specific humidity as input in the initial conditions. The more hydrometeor species used in the initial conditions of the forecast model, the more skillful becomes its precipitation forecast in the initial hours. Another set of experiments using liquid- and ice-phase hydrometeors separately shows that initialization with ice-phase hydrometers would be slightly more important to improving precipitation forecasts. Note that the results reported here should be understood as valid under the specific experimental configuration in this paper.

Barotropic theory for the velocity profile of Jupiter’s turbulent jets: an example for an exact turbulent closure

We model the dynamics of Jupiter’s jets by the stochastic barotropic $\unicode[STIX]{x1D6FD}$-plane model. In this simple framework, by analytic computation of the averaged effect of eddies, we obtain three new explicit results about the equilibrium structure of jets. First we obtain a very simple explicit relation between the Reynolds stresses, the energy injection rate and the averaged velocity shear. This predicts the averaged velocity profile far from the jet edges (extrema of zonal velocity). Our approach takes advantage of a time-scale separation between the inertial dynamics on one hand, and the spin-up (or spin-down) time on the other. Second, a specific asymptotic expansion close to the eastward jet extremum explains the formation of a cusp at the scale of energy injection, characterized by a curvature that is independent of the forcing spectrum. Finally, we derive equations that describe the evolution of the westward tip of the jets. The analysis of these equations is consistent with the previously discussed picture of barotropic adjustment, explaining the relation between the westward jet curvature and the $\unicode[STIX]{x1D6FD}$-effect. Our results give a consistent overall theory of the stationary velocity profile of inertial barotropic zonal jets, in the limit of small-scale forcing.

The Impact of Spatial Resolution, Land Use, and Spinup Time on Resolving Spatial Precipitation Patterns in the Himalayas

Abstract Frequently used gridded meteorological datasets poorly represent precipitation in the Himalayas because of their relatively low spatial resolution and the associated representation of the complex topography. Dynamical downscaling using high-resolution atmospheric models may improve the accuracy and quality of the precipitation fields. However, most physical parameterization schemes are designed for a spatial resolution coarser than 1 km. In this study the Weather Research and Forecasting (WRF) Model is used to determine which resolution is required to most accurately simulate monsoon and winter precipitation, 2-m temperature, and wind fields in the Nepalese Himalayas. Four model nests are set up with spatial resolutions of 25, 5, 1, and 0.5 km, respectively, and a typical 10-day period in summer and winter in 2014 are simulated. The model output is compared with observational data obtained from automatic weather stations, pluviometers, and tipping buckets in the Langtang catchment. Results show that, despite issues with the quality of the observational data due to undercatch of snowfall, the highest resolution of 500 m does provide the best match with the observations and gives the most plausible spatial distribution of precipitation. The quality of the wind and temperature fields is also improved, whereby the cold temperature bias is decreased. Our results further elucidate the performance of WRF at high resolution and demonstrate the importance of accurate surface boundary conditions and spinup time for simulating precipitation. Furthermore, they suggest that future modeling studies of High Mountain Asia should consider a subkilometer grid for accurately estimating local meteorological variability.

Effects of bottom topography on the spin-up in a cylinder

Motivated by better understanding the long-standing issue of the role of topography on the transport of angular momentum in rapidly rotating fluids, we conducted spin-up experiments in a straight cylinder with a regular pavement of rectangular blocks at the bottom. We perform particle image velocimetry measurements to monitor the decay of the initial differential motion generated by the sudden increase of the container rotation rate. We observe that the re-synchronization time, the so-called spin-up time, is shorter in the presence of topography with a minimum at a particular length scale of the topography pattern. We show evidence of energy transport by inertial waves as well as non-linear mechanisms leading to a scaling of the spin-up time significantly different from the classical E−1/2 in the absence of topography.

Technical Note: An efficient method for accelerating the spin-up process for process-based biogeochemistry models

Abstract. To better understand the role of terrestrial ecosystems in the global carbon cycle and their feedbacks to the global climate system, process-based biogeochemistry models need to be improved with respect to model parameterization and model structure. To achieve these improvements, the spin-up time for those differential equation-based models needs to be shortened. Here, an algorithm for a fast spin-up was developed by finding the exact solution of a linearized system representing the cyclo-stationary state of a model and implemented in a biogeochemistry model, the Terrestrial Ecosystem Model (TEM). With the new spin-up algorithm, we showed that the model reached a steady state in less than 10 years of computing time, while the original method requires more than 200 years on average of model run. For the test sites with five different plant functional types, the new method saves over 90 % of the original spin-up time in site-level simulations. In North American simulations, average spin-up time savings for all grid cells is 85 % for either the daily or monthly version of TEM. The developed spin-up method shall be used for future quantification of carbon dynamics at fine spatial and temporal scales.