Brandt Wdb 1001 Manual

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It covers the servicing, maintenance and repair of the product. Exploded views allow to identify all the part numbers and associated parts with the product in case they need to be replaced. This manual includes a description of the functions and capabilities and presents instructions as step-by-step procedures. Error codes and the Reference manual can also be included. Recent search for BRANDT Wdb 1001. Gratis Manual.se. 2011. maj. 24. BRANDT WTC1398K, BRANDT WTD1051K.Manuale PDF.it., Brandt WTD 1251 K,,,,,, Anvandarmanual BRANDT WTD1171A: Agarmanualer, Anvandarguider BRANDT Spares and Spare Parts. Find your BRANDT Part. Gratis Handleiding.nl. BRANDT WTD 1251 K. Descarcare de PDF de BRANDT WTD1251K, ghid de utilizare BRANDT WTD1251K, manual de operatii BRANDT. Brandt WTD 6284 K Brandt BFV 1508.,, Brandt BFV Mode d'emploi BRANDT WTD1171A: Notice d'Utilisation, Mode d'emploi, Manuel descargar pdf BRANDT WTD1251K, Guia del usuario BRANDT WTD1251K, Operating Instructions BRANDT. Manual Instrucciones.es. SUPPLY PIPE for BRANDT Model WTD1251K Washing Machine., Brandt WM 1001 ECO User Manual BRANDT WTD1171A: Owner's Manual, User Guide, Operating Instructions and Manual for WTD1171A. Including Cutlery Baskets, Dispensers, Door Lock, Elements, Filters, Hinges, Hoses, Knobs, Motors, Pumps, Download PDF BRANDT WTD1251K, Handbuch BRANDT WTD1251K, Handbetrieb BRANDT. Meine Bedienungsanleitung.de. Telecharger la Notice d'Utilisation pour BRANDT WTD1251K, le Mode d'Emploi pour BRANDT WTD1251K, Manuel d'Utilisation pour BRANDT.Log in We need to verify that you are not a robot generating spam. I have read and accept the Wiley Online Library Terms and Conditions of Use Shareable Link Use the link below to share a full-text version of this article with your friends and colleagues. Learn more. Copy URL Snowfall is measured by a combination of snow pillows, snow courses, and rain gauges.
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However, the paucity of locations of these measurements, particularly at high elevations, can introduce artifacts into precipitation estimates that are detrimental for hydrologic forecasting. To reduce errors, we need high?resolution, spatially complete measurements of precipitation. Remotely sensed snow depth and snow water equivalent (SWE), with retrieval time scales that resolve storms, could help mitigate this problem in snow?dominated watersheds. Since 2013, National Aeronautics and Space Administration's Airborne Snow Observatory (ASO) has measured snow depth in the Tuolumne basin of California's Sierra Nevada to advance streamflow forecasting through improved estimates of SWE. In early April 2015, two flights 6 days apart bracketed a single storm. The work herein documents a new use for ASO and presents a methodology to directly measure the spatial variability of frozen precipitation. In an end?to?end analysis, we also compare gauge?interpolated and dynamically downscaled estimates of precipitation for the given storm with that of the ASO change in SWE. The work shows that the extension of ASO operations to additional storms could benefit our understanding of mountain hydrometeorology by delivering observations that can truly evaluate the spatial distribution of snowfall for both statistical and numerical models. The dynamic way in which the atmosphere and land interact generates highly variable precipitation rates, particularly in mountain landscapes. To quantify precipitation, we currently use a network of gauges at fixed locations, and to make these measurements more meaningful, we use statistics or models to “fill the blanks.” However, because the “blanks” often incorporate mountain peaks and valleys, it is challenging, if not impossible, to discern the accuracy and precision of our models and understanding of the process. Consequently, we require new ways of measuring mountain precipitation.
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Unlike rain, snow remains roughly in place following snowfall, thereby preserving the distribution of precipitation. Airborne lidar can now measure these changes in depth after storms at high accuracy and precision. Here we present a methodology that uses this technological advancement to map the variability in precipitation during a snowstorm. Ultimately, these techniques, if extended to additional storms in various regions around the world, could help to evaluate precipitation in weather models by improving understanding of mountain precipitation. Yet estimates of the total amount of water held in this environment are uncertain because of limited in situ sampling and technological challenges associated with measuring mixed phase precipitation. This fundamental problem for both the field of hydrology and humanity limits our ability to effectively manage water and mitigate hazards. These problems aside, gauge data are used to interpolate and extrapolate precipitation estimates or to validate dynamically downscaled precipitation estimates from weather models. These modeled estimates are then used as the primary forcings for both hydrologic models and runoff forecasts, and any error embedded in the precipitation measurement of course propagates, thus confounding the prediction (Kirchner, 2006 ).However, ground?based radar suffers from terrain blocking, beam spreading, the inability to observe precipitation near the surface (Mott et al., 2018; Scipion et al., 2013 ), and ambiguities owing to different scattering properties of water and ice (Austin, 1987 ). Furthermore, spaced?based radiometers and radars lack the spatial resolution for small. In snow?dominated watersheds, the remote sensing of snow depth provides a reasonable solution to this fundamental problem.The Tuolumne River basin has the most temporally complete record in California's central Sierra Nevada (Figure 1 ).
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Principally, ASO monitors snow depth and snow albedo throughout the melt season for streamflow forecasting, but in early April 2015 two ASO flights 6 days apart bracketed a single storm event, on which this study focuses.However, the April 2015 storm studied herein was cold and possessed a short revisit period between flights (6 days) that limited snowmelt and other confounding processes. It therefore provides an excellent test case to explore how differential airborne lidar and snow modeling can combine to reconstruct high?resolution spatial estimates of snowfall's change in the snow water equivalent ( ?SWE ).This infrequency of precipitation in California necessitates the need for improved understanding of the spatial distribution of precipitation and the mechanisms that drive its variability.The reservoir lies at 1,200 m above sea level, has a contributing catchment area of 1,175 km 2, and can store up to 0.442 km 3 of water for 2.7 million people living in the greater San Francisco Bay Area. Slopes generally face northwest, southeast, and due west.ASO uses two primary instruments a Riegl Q1560 airborne laser scanner and an ITRES CASI 1500 imaging spectrometer (Painter et al., 2016 ). The airborne laser scanner measures the snow depth by subtracting snow?free elevations from the winter's snow surface, with the algorithm refined for accurate retrievals in forested areas. Standard snow depth products are further constrained using the spectrometer retrievals and are provided at 3?m grid spacing. The spectrometer measures the reflected radiance across wavelengths from 380 to 1,050 nm at 10 nm nominal spectral resolution, from which snow?covered area, spectral albedo, snow grain size, and radiative forcing by light?absorbing particles are calculated.The pillows directly measure pressure changes, thereby integrating snowfall, melt, sublimation, and wind redistribution. The instruments are generally located in nearly flat, sheltered forest clearings and report hourly.
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Data are posted to the California Data Exchange ( ) at hourly and daily resolutions. In this study we used hourly data because the temporal resolution is better suited to estimating the change in SWE from a storm. Changes in pillow SWE result only from changes in mass. Therefore, new snow accumulation or rain on snow that freezes or fails to drain will increase measured SWE. Decreases in SWE result from melt, sublimation, and scouring from wind.The acoustic snow depth sensors are not always located above the snow pillows, so a snow depth measurement does not necessarily correspond to the pillow SWE and thus limits snow density calculations from these data. Snow depth can increase because of snowfall or wind redistribution and can decline because of snowmelt, sublimation, wind scouring, or compaction. Finally, during snowfall or windy periods, acoustic reflections from airborne snow crystals can cause snow depth data to be noisy (Bair et al., 2018 ). Table 1 summarizes in situ data from snow pillows and depth sensors used in this study.Initially, both SWE and snow depth data underwent manual quality control for spurious values. SWE and depth values were then smoothed using a robust discretized spline, which filled the data gaps (5 of the data) but preserved the overall shape of the time series (Garcia, 2010 ).These data were used to help assess whether the storm produced rain during the period of analysis. This array of precipitation products represents the current breadth of operational precipitation estimation capabilities.The product has a 4 km spatial resolution and does not parse rain and snow. Purely to understand the “normal” precipitation regime, we also utilize the PRISM climatology, a 30?year monthly mean estimate of the precipitation between 1981 and 2010. The CNRFC QPE has a 6 hr temporal resolution and a 4 km spatial resolution.The model is configured with 60 vertical levels and a 10 hPa model top.
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The ERA5 reanalysis, produced by the European Centre for Medium Range Weather Forecasts, provides the initial and lateral boundary conditions (National Center for Atmospheric Research Data Archive, 2019 ) and is also evaluated to define the event's synoptic meteorology. All simulations were initialized on 2 April 2015 at 0:00 UTC and were terminated on 9 April 2015 at 23:00 UTC. This explicit configuration is based upon “West?WRF,” a near?real?time configuration run by the Center for Western Weather and Water Extremes, which has been validated over California for landfalling atmospheric rivers (Martin et al., 2018 ). We additionally performed a small ensemble of eight different simulations with varying microphysics and PBL schemes to demonstrate uncertainty in precipitation distribution related to model physics. The West?WRF physics configuration, changes to ensemble members, and reasoning for these choices are described in the supporting information (Text S1 ). Rainfall amounts were calculated by subtracting SNOWNC from RAINNC. From hereon, we refer to WRF's SNOWNC as “snow” and WRF's RAINNC as “precipitation.”We used these data to assess the potential for preferential and postdepositional wind redistribution along with 10 m U and V wind vectors from West?WRF.Given the low amounts of snow during water year 2015, the expansion of the snow extent poststorm provides an approximation of the rain?snow transition. The DEM was also used to calculate slope and aspect.However, the subtraction assumes that the density of the newly fallen snow is equivalent to the bulk snow density estimates that are used to convert measured snow depth to SWE in the ASO processing pipeline. While new snow density is accounted for in the ASO processing through the iSnobal model, a simple product subtraction cannot distinguish between new and existing water equivalent as the new snow density and bulk density are not the same.
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Over time, the difference between storm and bulk density will decrease as the new snow is mechanically compacted (Chen et al., 2011 ) and metamorphoses (Colbeck, 1982, 1998 ).However, depending on the storm and the time between survey flights, ?SD is not only a function of the snow accumulation but also changes with time due to melt, sublimation, and compaction (Figure 2 ). ?SWE can be expressed asWind redistribution can also impact the ?SWE by adding or subtracting snow from any given pixel. However, wind redistribution is highly spatially variable and operates on scales shorter than 100 m (Mott et al., 2018; Winstral et al., 2009 ). At larger spatial scales ( ? 1 km) the wind redistribution can be implicitly, versus explicitly, represented within aggregated ?SWE pixels (Deems et al., 2006; Mott et al., 2018 ).Between points 1 and 2: Compaction, sublimation, and melt could distort snow depths. Between points 2 and 3, precipitation accumulation is the dominant forcing on snow depth. Between points 3 and 4, snow depths can be influenced by compaction, melt, and sublimation. During all periods, wind redistribution can be operating. The difference between points 3 and 2 is the newly accumulated snow depth. Point 3 is referred to hereon as the peak snow depth. If declines in SWE are observed, then sublimation or melt or their combination could cause the mass loss. If mass losses are recorded at snow pillows, then a physically based, spatially distributed model can be used to help estimate the magnitude of each flux. In these surveys the snow depth fields possess a positively skewed, long?tailed distribution. To reduce the error in the snowfall calculation, we omitted snow depths above a threshold of 6 m. The threshold was picked after a careful inspection of snow depths in known deeper snow pockets, for example, in the leeside of cirques. This step removed 0.006 of the 3 April snow depths and 0.05 of the 9 April snow depths.
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However, the SWE remained relatively constant post accumulation. This indicates that snow densification caused the decline in snow depth post accumulation. Compaction of new snow can be driven by new snow overburden pressure (Kojima, 1967 ) and by snow grain sintering (Colbeck, 1982, 1998 ). The post?storm ASO survey on 9 April occurred after compaction (Point 4 in Figure 2 b); therefore, to reconstruct the storm's total accumulation (Point 3 in Figure 2 b), we applied a compaction correction to the ?SD, which enables the use of a single new snow density estimate in the precipitation estimation.To account for compaction losses and attempt to scale the loss in snow depth across the Tuolumne, we first compiled data from 48 snow stations across the Sierra Nevada (Table S2 ), where snow depth, temperature, and in most cases SWE were measured and regressed the compaction using both linear and nonlinear techniques with elevation, latitude, distance to the Sierra Nevada crest, accumulated snow depth, change in SWE, and the mean temperature during the storm as independent variables. Compaction (linear and nonlinear) was well correlated only with the newly accumulated snow depth—the unknown in this case (Figure S2 and Text S2 )—restricting its application here.The simplest method (using the linear relationship between the snow depth at the time of the second ASO fight and the peak snow depth) proved to have the least error when compared to the peak snow depth (Figure S3 and Table S3 ). These results suggest that for this storm, compaction rates exhibited linear declines at all elevations in the basin (see Figures S3a and S3b ).SWE variability using reasonable estimates of the bias and random noise. This error estimate is then both added and subtracted from the ASO?adjusted ?SD to generate high and low estimates of the adjusted ?SD. If the low estimate of the adjusted ?SD was negative, we set the pixel's accumulated snow depth to 0.
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This process bounded the snowfall measurement with confidence intervals based on estimates of uncertainty and helps assess signal?to?noise ratio within the lidar measured ?SD. We believe these error estimates to be conservative given the measured depth error of 0.08 m at the 3 m grid resolution (Painter et al., 2016 ), which under typical assumptions of normal error distributions for lidar elevations, reduces to under 2 cm with the grid coarsening from 3 to 50 m.We used equations developed for iSnobal to spatially estimate the new snow density at a 50 m resolution. New snow densities were only calculated during the main accumulation phase of the storm (7 April 00:00 through 8 April 00:00 PST), and since WRF was run with multiple physics schemes, we used the ensemble to calculate a median spatially distributed new snow density for each grid point.Vapor pressure e isR is the ideal gas constant, M w is the molecular weight of water, and L v is the enthalpy of vaporization. The hourly new snow density can then be estimated using equations developed from Susong et al. ( 1999 ):We also recorded the interquartile range for each grid point as a measure of the uncertainty within the new snow density. The combination of the new snow density and the ASO?adjusted ?SD generated an estimate of the storm?deposited ?SWE and its uncertainty.To this end, we investigated how well WRF's 2 m temperature compared to surface observations at 15 high?elevation surface stations (Table S4 ) within or near the Tuolumne.Because these considerations are critical for both a storm's precipitation magnitude and spatial distribution, we describe the meteorological conditions leading up to and during the April 2015 storm.While precipitable water values were relatively low for a California winter storm and did not meet atmospheric river criteria based on the Rutz et al.
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( 2014 ) catalog, the following conditions appear to have contributed to heavier Sierra Nevada snowfall: (1) a moderate southwesterly integrated vapor transport generated by strong low?level winds, (2) weak static stability and relatively cold temperatures in the region of enhanced moisture transport, and (3) colocated dynamical forcing for ascent (Cannon et al., 2018 ). Although the largest Sierra storms occur in association with atmospheric rivers (Ralph et al., 2016 ), this event demonstrates that heavy snowfall can also be generated through enhanced dynamical forcing despite comparatively little available moisture. While events of this nature in California are less often discussed in the scientific literature, they are known to the local forecasting community and are noteworthy because they can produce a more uniform distribution of precipitation (Houze, 2012; Katzfey, 1995; Lee, 1984; Oakley et al., 2018 ).The distribution represents 60 of total precipitation, equivalent to the top 300 events between 1981 and 2017. The April 2015 storm is demarcated in red and exhibits a more even distribution of precipitation across all elevations relative to the event average and the storms that were mapped by Behrangi et al. ( 2018 ), which includes the 8 and 21 February 2017 storms. Finally, the PRISM climatology clearly exhibits strong orographically enhanced precipitation, particularly on the northern edges of the Tuolumne. The April 2015 storm does not conform to the PRISM climatology, which assumes strong orographic enhancement (Figure 3 d).As such, a careful assessment of the atmospheric melt elevation is required, which we attempt using a combination of snow?level radars, in situ data, satellite imagery, and West?WRF. All three snow?level radar sites are located on the windward slope of the Sierra Nevada, but at elevations that range between 184 and 643 m. Based on theory (Minder et al.

, 2011 ), we would expect atmospheric melting elevations to decrease the closer one is to the Sierra Nevada crest. The result is consistent with theory and the results from Minder and Kingsmill ( 2013 ).The slight rise in inflows indicates the presence of rain, albeit rain that would have been spatially limited and brief. This notion is enforced by model results from West?WRF. West?WRF did model rain (RAINNC minus SNOWNC), but liquid equivalent was constrained to Hetch Hetchy reservoir itself (Figure S5 ). The minimal basin runoff into the reservoir, combined with evidence collected from Landsat, snow?level radars, and WRF, indicates that snow predominated in the Tuolumne throughout the storm. Additionally, onsite records confirmed that snow was present on the mornings of 6 and 8 April at the reservoir. We acknowledge that at the lowest elevations of the basin, rain may have impacted snow depth measurements during warm front passage, but our investigation suggests that the impacts are likely negligible at the basin scale.These data indicate that little to no melt or sublimation occurred before or after the April 2015 storm, negating the need to spatially model these fluxes. Since melt and sublimation were minimal and wind redistribution would have reduced both SWE and depth, the most likely process responsible for the decline in snow depth is compaction, for which our methodology compensated (see section 4.2 ).Air pressure and density exhibit the same oscillating pattern within the SWE signal. Thus, the changing SWE at the Dana Meadows snow pillow is not due to changes in mass at the pillow but rather changes in air density.

While the site lies on the leeside of high topography and therefore is prone to differing physical processes related to snowfall, we include these data for three reasons: (1) the proximity of the measurement; (2) systematic manual measurements of new snow density are rare and MMSP documents the measurement; and (3) given the relatively uniform precipitation distribution observed in this particular storm, the MMSP data should inform the estimate of the density.Many of the local snow pillows, both within and outside the basin boundary, quantitatively confirmed the magnitude of the event (Figure 6 c). These results support our methodology and the estimate of the new snow density.Western facing slopes received most of the precipitation (36, Figure S7b ), but western and southern facing slopes predominate the basin aspects. The ?SWE is insensitive to aspect as both Figure 6 b displays, and the four aspect medians demonstrate 29 (N), 25 (E), 27 (W), and 21 mm (S). Had the storm been much more orographically enhanced, one would have expected southern and western facing slopes to have exhibited deeper amounts of ?SWE relative to other aspects.When compared to the ASO estimate, many of the precipitation products produced too much snow, particularly on the northern side of the basin.The difference between the WRF precipitation and snow estimates is rain.However, a key takeaway is how sensitive precipitation estimates are to the model used, and that the ASO observations clearly identify precipitation distributions that do not appear to be physically coherent with the observed signals. Notably, PRISM is too heavily weighted toward the climatological distribution of orographic enhancement, which CNRFC forecaster guidance apparently corrected for in near?real time (and is noted in the Area Forecast Discussions from that date).

It is also apparent that the more advanced WRF Morrison and Thompson microphysics schemes performed well in this specific case, but WRF precipitation is sensitive to the choice of the microphysics package and the PBL scheme. WRF can produce both total precipitation and snowfall estimates—the difference between the two being liquid precipitation. For the analysis we used the total precipitation, but by separating the two in Figure 7 k we show how, depending on the WRF microphysics package and PBL scheme used, the total and spatial distribution of water equivalent will differ. The variability across WRF's different physics options is consistent with other studies (Garvert et al., 2007; Jankov et al., 2007 ).A few of the precipitation products perform well at the higher elevations of the basin. Many of the data points in Figure 8 sit close to or on the one?to?one line (e.g., Figures 8 b, 8 c, 8 h, and 8 j). Unexpectedly, much of the discrepancy between data sets and ASO occurs across the midelevations of the basin. Finally, in all cases the precipitation products modeled more precipitation than ASO at the lowest elevations of the basin. This result was expected due to the potential for rain at the lowest elevation and because Hetch Hetchy reservoir was snow?free during the storm and therefore not a “measurable” area using ASO.Statistics included are the mean absolute error (MAE), the correlation coefficient (Corr.), and the total percent error (per. Err.).In just this specific case, it is clear that had we validated WRF based on PRISM rather than ASO, we would have had a different perspective on the performance of microphysics schemes in this storm. According to the West?WRF configuration, wind speeds peaked when the precipitation rate was maximized (Figure 9 a). Additionally, winds occurred from the south?southwest, which is consistent with measurements from 28 local surface station measurements from the San Joaquin hydrologic region (Figures 9 c and 9 d).

Care must be used when comparing WRF surface wind speeds with surface stations, as WRF uses a 1 km DEM. Nonetheless, preferential deposition in this case should produce greater deposition on north facing slopes due to the wind predominantly guesting from the south. This spatial anomaly is readily observable in the ASO?adjusted ?SD at 3?m spatial resolution (Figure 9 e).Higher ?SD 's can be found on north facing slopes (blue regions), an indicator of preferential deposition due to southerly winds during the storm.In all cases, transects failed to yield a clear correlation between ?SWE and elevation, slope, tree height, or land cover type.For storms that are more orographically driven, it is possible that aspect and slope would produce a different distribution of local?scale precipitation enhancement and depletion.The problem arises from the sparsity of surface station networks that are inadequate to quantify the dynamic, chaotic, and intermittent precipitation processes in complex terrain. In snow?dominated watersheds, remote sensing of snow properties can provide the spatially explicit and extensive data to quantify event precipitation across the entire landscape and fill the gaps between stations. Additionally, the data yield an independent validation data set for gauge?interpolated and extrapolated precipitation products and for dynamically downscaled precipitation models. ASO acquisitions bracketed the storm, and we used these data to derive a spatially explicit accumulated change in snow water equivalent ( ?SWE ), which is a relatively direct measurement of solid precipitation from the event. We found the distribution of ?SWE to be somewhat uniform across many of the basin elevations and aspects. These data supported the general pattern of precipitation suggested by the synoptic meteorology and demonstrated the potential of ASO for snowfall monitoring. We also compared the ?SWE with estimates from 10 precipitation products including two gauge?

based interpolations (PRISM and CNRFC QPE) and 8 dynamically downscaled WRF simulations that used 6 different microphysics schemes and 3 different PBL schemes. The various products exhibited a high degree of variability in their precipitation amounts and spatial distribution. A few simulations of high elevation precipitation amounts compared well to the ?SWE, but most tended to overestimate the amount of precipitation in the midelevations of the basin.Such a measurement campaign should cover the duration and course of several storms across climatic, topographic, and vegetation gradients.Application of this snowfall monitoring technique to quantify many types of storms, spanning the spectrum of convective through stable systems, would provide value to the synoptic and precipitation modeling communities. By directly observing the snowfall distribution, ASO data provide validation of the hydrometeorology across a whole basin or mountain region. Research that utilizes the technology to investigate multiple storms, spanning a variety of snow climates, would enhance our understanding of precipitation mechanisms and thereby improve how statistical methods, weather models, and climate models represent mountain precipitation. Many of the data in this paper are publicly available. Airborne Snow Observatory data can be found at the National Snow and Ice Data Center ( ). The California Data Exchange Center snow page provides data on SWE and depth. The meteorological data used in this study came from MesoWest ( ), where data for a station can be downloaded without a login. PRISM data can be downloaded from the website ( ), and the CNRFC QPE can be obtained from. Finally, West?WRF output is available on request from the Center for Western Weather and Water Extremes. To make the duplication of the work readily possible, we have also deposited all of the data in Zenodo—a fair compliant repository; doi: ). We greatly appreciate the work of ASO's compute and flight operation team.