WC % of all observations % of bias observations Ineligible for Bias Correction 22.69 N/A (insufficient training data) Failed Validity Check 0.09 0.11 Failed Statistical Check 0.47 0.6 Failed RBF Check 49.58 64.12 Completed Post-Processing 27.18 35.16 and passed all QC checks 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 analysis compares well with both the METAR and MESONET analysis. The uWx magnitude of both the cross and alongbarrier pressure gradient roughly equivalent in the uWx, METAR, and MESONET analyses. The analysis, like the METAR analysis, is smoother uWx than the MESONET analysis. A less homogeneous analysis could be generated by relaxing uWx
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