100,000 observations an hour. In August 2016, The Weather Channel app, with over 50 million downloads, began collecting upwards of 3 million observations an hour from a mere 3% of their user-base. By mid-2017, 30 billion pressures will be retrieved every month from the Weather Channel app. If infrastructure and data quality challenges can be met, these observations could revolutionize numerical weather prediction (NWP) by providing unprecedented observational coverage and density. 1.2) Background and Literature Review As of January 2017, over 170 smartphones are capable of measuring surface pressure. Surface pressure is arguably the most valuable observed surface variable due to its ability to provide information on all scales of motion ranging from convectively induced cold pools to mid-latitude cyclones. Surface pressure atmospheric structure aloft above the planetary boundary layer (PBL), surface temperature, moisture, and wind. Unlike other surface measurements, surface pressure is less affected by error as the distribution of surface pressure is typically more spatially homogeneous than other surface parameters. Measurements of pressure exhibit fewer exposure problems and are less sensitive to the placement of instrumentation than measurements of other surface variables. For these reasons, among others, a number of a studies during the last decade have examined the impact of surface observations, and in particular surface pressure, on numerical simulations and forecasts across a variety of atmospheric scales. At the planetary scale, Whitaker et al. (2004) showed that assimilating surface pressure at a relatively sparse collection of locations could produce realistic lower and middle tropospheric analyss with 500 hPa errors similar to present 2.5 day forecast errors. Compo et al. (2006) assimilated surface pressure observations using ensemble techniques and found surface pressure observations alone could reproduce the entire upper tropospheric circulation, even during the
availability of millions, and potentially, billions of SPOs from providers like the Weather Company a global pressure network of unprecedented density has become a feasible. Lei et al. (2014) and Anderson et al. (2005) suggested a monotonic relationship between observation density and prior error. If even a fraction of smartphone observations can be utilized for NWP their superior spatial coverage could provide significant improvements in forecast skill. Unfortunately, no apps SPOs suitable for NWP. Incoherency and a lack of consistency among SPOs currently NWP. If smartphones are to contribute to NWP and become a viable atmospheric observing platform, sources of observational uncertainty must be constrained and quantified. . Chapter 2 DEVELOPMENT AND PERFORMANCE OF AN INNOVATIVE PRESSURE COLLECTION APP 2.1) In-app quality control framework 2.1.1 Motivation A quality control (QC) framework was developed within the app to reduce observational error, both systematic and stochastic, quantify uncertainty and bias, and provide QC estimates of pressure change. Previous SPO providers such as PressureNet and OpenSignal performed no
dependent spatial consistency checks. Random forests are only trained on phones that have submitted at least fifty observations. Out of all the phones in the uWx network less than 15% fail to meet this requirement for bias-correction. Overall, the random forests do a remarkable job at predicting and correcting uWx phone bias (18). Even before QC checks are applied bias-corrected uWx observations already appear
WC % of all observations % of bias observations Ineligible for Bias Correction (insufficient training data) 22.69 N/A Failed Validity Check 0.09 0.11 Failed Statistical Check 0.47 0.6 Failed RBF Check 49.58 64.12 Completed Post-Processing and passed all QC checks 27.18 35.16 Over 60% of bias-corrected observations are removed by the spatially filtered RBF check, with less than 1% removed by statistical and validity checks. The majority of the removed observations are in urban areas, however a notable amount are also removed from roadways and more rural areas. The limited frequency of observation retrieval also contributes to the reduction of viable observations. The random forest algorithm is only performed on phones from which at least fifty observations have been retrieved. This criterion results in over 20 % of WC phones being ineligible for post-processing. In total, only 35% of bias-corrected WC observations are included in the final altimeter setting analysis. While post-processing bias-corrected observations can produce realistic altimeter analyss it remains unclear whether these analyses are accurate. To evaluate the accuracy of the altimeter analysis displayed in Fig 18-19, a comparison was made between conventional in-situ observations and phone altimeter observations (Fig. 20). The uWx analysis compares well with both the METAR and MESONET analysis. The magnitude of both the cross and alongbarrier pressure gradient roughly equivalent in the uWx, METAR, and MESONET analyses. The uWx analysis, like the METAR analysis, is smoother than the MESONET analysis. A less homogeneous uWx analysis could be generated by relaxing
precipitation began. The surface low achieved its maximum strength just after making landfall around UTC. This case was selected as the operational High Resolution Rapid Refresh (HRRR) failed to capture the timing and strength of the wind-ramp event in the WFIP2 study region. Figure 23, shows a timeheight cross-section from a SODAR near Rufus, Oregon. The HRRR brings strong winds down to the surface at 15 UTC, while the SODAR reveals that stronger winds did not occur in the rotor layer (80-150m) until 18 UTC. A similar two-hour timing error was also observed by a radar wind profiler at nearby Boardman, Oregon (not shown). Aside from the frontal timing issue, the HRRR in general overestimates the strength of low-level winds. This is likely due to overmixing in the PBL. Failing to correctly time the onset of strong low-level winds poses a challenge to wind energy providers, who seek to anticipate when an up-ramp in wind energy production will occur. One goal of this study will be to determine whether SPOs can constrain near-surface wind forecasts during an up-ramp event of interest to wind-energy stakeholders. A second case-study unrelated to WFIP2 was examined in mid-October. As mentioned previously, it was around this time that the number of uWx users doubled from approximately 1000 to over 2000 active users. On October 12th, extended forecasts from the University of Washington’s